book.bib

@book{Bondy2008GraphTheory,
    title = {{Graph Theory}},
    year = {2008},
    booktitle = {Graph Theory},
    author = {Bondy, J. A. and Murty, U. S. R.},
    pages = {651},
    publisher = {Springer},
    isbn = {9781846289699},
    doi = {10.1007/978-1-84638-970-5},
    issn = {1079-7114},
    pmid = {21929175},
    arxivId = {arXiv:1102.1087v6},
    keywords = {affordance, motivation, navigation, reinforcement learning}
}

@article{Barrat2003TheNetworks,
    title = {{The architecture of complex weighted networks}},
    year = {2003},
    journal = {Proceedings of the National Academy of Sciences},
    author = {Barrat, Alain and Barthelemy, Marc and Pastor-Satorras, Romualdo and Vespignani, Alessandro},
    number = {11},
    month = {3},
    pages = {3747--3752},
    volume = {101},
    url = {http://www.pnas.org/cgi/doi/10.1073/pnas.0400087101 http://arxiv.org/abs/cond-mat/0311416%0Ahttp://dx.doi.org/10.1073/pnas.0400087101},
    isbn = {0027-8424 (Print)},
    doi = {10.1073/pnas.0400087101},
    issn = {0027-8424},
    pmid = {15007165},
    arxivId = {cond-mat/0311416}
}

@article{Albert2005Scale-freeBiology,
    title = {{Scale-free networks in cell biology}},
    year = {2005},
    journal = {Journal of Cell Science},
    author = {Albert, R.},
    number = {21},
    pages = {4947--4957},
    volume = {118},
    url = {http://jcs.biologists.org/cgi/doi/10.1242/jcs.02714},
    isbn = {0021-9533, 1477-9137},
    doi = {10.1242/jcs.02714},
    issn = {0021-9533},
    pmid = {16254242},
    arxivId = {q-bio/0510054}
}

@article{Li2009,
    title = {{Network module detection: Affinity search technique with the multi-node topological overlap measure}},
    year = {2009},
    journal = {BMC Research Notes},
    author = {Li, Ai and Horvath, Steve},
    number = {1},
    month = {7},
    pages = {142},
    volume = {2},
    publisher = {BioMed Central},
    url = {http://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-2-142 http://www.ncbi.nlm.nih.gov/pubmed/19619323 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2727520},
    isbn = {1367-4811},
    doi = {10.1186/1756-0500-2-142},
    issn = {17560500},
    pmid = {19619323},
    keywords = {Biomedicine general, Life Sciences, Medicine/Public Health, general}
}

@article{Ahn2010,
abstract = {Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society1-3. One crucial step when studying the structure and dynamics of networks is to identify communities4-5: groups of related nodes that correspond to functional subunits such as protein complexes6,7 or social spheres8-10. Communities in networks often overlap9-10 such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure11-13. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction6,7,14 and metabolic networks 11,15,16, and show that a large social network10,17,18 contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon. {\textcopyright} 2010 Macmillan Publishers Limited. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {0903.3178},
author = {Ahn, Yong Yeol and Bagrow, James P. and Lehmann, Sune},
doi = {10.1038/nature09182},
eprint = {0903.3178},
issn = {00280836},
journal = {Nature},
pmid = {20562860},
title = {{Link communities reveal multiscale complexity in networks}},
year = {2010}
}

@article{Alanis-Lobato2017,
abstract = {The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein-protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein-Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.},
author = {Alanis-Lobato, Gregorio and Andrade-Navarro, Miguel A and Schaefer, Martin H},
doi = {10.1093/nar/gkw985},
issn = {1362-4962},
journal = {Nucleic acids research},
number = {D1},
pages = {D408--D414},
pmid = {27794551},
title = {{HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/27794551 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5210659},
volume = {45},
year = {2017}
}

@article{Alonso-Lopez2019a,
abstract = {{\textcopyright} 2019 The Author(s). The collection and integration of all the known protein-protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of 'protein interactomes' for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the 'ontology and controlled vocabulary': www.ebi.ac.uk/ols/ontologies/mi; developed by 'HUPO PSI-MI').Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) 'binary' physical direct detection methods and (ii) 'indirect' methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific 'experimental evidences' that support each PPI, indicating whether the interactions can be considered 'binary' (i.e. supported by at least one binary detection method) or not.},
author = {Alonso-L{\'{o}}pez, DIego and Campos-Laborie, Francisco J. and Guti{\'{e}}rrez, Miguel A. and Lambourne, Luke and Calderwood, Michael A. and Vidal, Marc and {De Las Rivas}, Javier},
doi = {10.1093/database/baz005},
file = {:Users/italodovalle/Documents/MendeleyDesktop/Alonso-L{\'{o}}pez et al/2019/Database/Alonso-L{\'{o}}pez et al.{\_}2019.pdf:pdf},
issn = {17580463},
journal = {Database},
number = {i},
pages = {1--8},
title = {{APID database: Redefining protein-protein interaction experimental evidences and binary interactomes}},
volume = {2019},
year = {2019}
}
@book{Barabasi2016,
address = {Cambridge, UK},
author = {Barab{\'{a}}si, Albert-L{\'{a}}slzo},
edition = {1},
pages = {1--475},
publisher = {Cambridge University Press},
title = {{Network Science}},
year = {2016}
}

@article{Borgatti2006ACentrality,
    title = {{A Graph-theoretic perspective on centrality}},
    year = {2006},
    journal = {Social Networks},
    author = {Borgatti, Stephen P. and Everett, Martin G.},
    number = {4},
    month = {10},
    pages = {466--484},
    volume = {28},
    publisher = {North-Holland},
    url = {https://www.sciencedirect.com/science/article/pii/S0378873305000833},
    doi = {10.1016/J.SOCNET.2005.11.005},
    issn = {0378-8733}
}
@article{Breuer2013,
abstract = {InnateDB (http://www.innatedb.com) is an integrated analysis platform that has been specifically designed to facilitate systems-level analyses of mammalian innate immunity networks, pathways and genes. In this article, we provide details of recent updates and improvements to the database. InnateDB now contains {\textgreater}196 000 human, mouse and bovine experimentally validated molecular interactions and 3000 pathway annotations of relevance to all mammalian cellular systems (i.e. not just immune relevant pathways and interactions). In addition, the InnateDB team has, to date, manually curated in excess of 18 000 molecular interactions of relevance to innate immunity, providing unprecedented insight into innate immunity networks, pathways and their component molecules. More recently, InnateDB has also initiated the curation of allergy- and asthma-related interactions. Furthermore, we report a range of improvements to our integrated bioinformatics solutions including web service access to InnateDB interaction data using Proteomics Standards Initiative Common Query Interface, enhanced Gene Ontology analysis for innate immunity, and the availability of new network visualizations tools. Finally, the recent integration of bovine data makes InnateDB the first integrated network analysis platform for this agriculturally important model organism.},
author = {Breuer, Karin and Foroushani, Amir K and Laird, Matthew R and Chen, Carol and Sribnaia, Anastasia and Lo, Raymond and Winsor, Geoffrey L and Hancock, Robert E W and Brinkman, Fiona S L and Lynn, David J},
doi = {10.1093/nar/gks1147},
issn = {1362-4962},
journal = {Nucleic acids research},
month = {jan},
number = {Database issue},
pages = {D1228--33},
pmid = {23180781},
title = {{InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23180781 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3531080},
volume = {41},
year = {2013}
}

@article{Cao2013,
    title = {{Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks}},
    year = {2013},
    journal = {PLoS ONE},
    author = {Cao, Mengfei and Zhang, Hao and Park, Jisoo and Daniels, Noah M. and Crovella, Mark E. and Cowen, Lenore J. and Hescott, Benjamin},
    doi = {10.1371/journal.pone.0076339},
    issn = {19326203}
}
@article{Chatr-Aryamontri2017,
abstract = {The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30{\%} increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.},
author = {Chatr-Aryamontri, Andrew and Oughtred, Rose and Boucher, Lorrie and Rust, Jennifer and Chang, Christie and Kolas, Nadine K and O'Donnell, Lara and Oster, Sara and Theesfeld, Chandra and Sellam, Adnane and Stark, Chris and Breitkreutz, Bobby-Joe and Dolinski, Kara and Tyers, Mike},
doi = {10.1093/nar/gkw1102},
issn = {1362-4962},
journal = {Nucleic acids research},
keywords = {interactome},
mendeley-tags = {interactome},
number = {D1},
pages = {D369--D379},
pmid = {27980099},
title = {{The BioGRID interaction database: 2017 update.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/27980099 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5210573},
volume = {45},
year = {2017}
}
@article{Cowley2012,
abstract = {The Protein Interaction Network Analysis (PINA) platform is a comprehensive web resource, which includes a database of unified protein-protein interaction data integrated from six manually curated public databases, and a set of built-in tools for network construction, filtering, analysis and visualization. The second version of PINA enhances its utility for studies of protein interactions at a network level, by including multiple collections of interaction modules identified by different clustering approaches from the whole network of protein interactions ('interactome') for six model organisms. All identified modules are fully annotated by enriched Gene Ontology terms, KEGG pathways, Pfam domains and the chemical and genetic perturbations collection from MSigDB. Moreover, a new tool is provided for module enrichment analysis in addition to simple query function. The interactome data are also available on the web site for further bioinformatics analysis. PINA is freely accessible at http://cbg.garvan.unsw.edu.au/pina/.},
author = {Cowley, Mark J and Pinese, Mark and Kassahn, Karin S and Waddell, Nic and Pearson, John V and Grimmond, Sean M and Biankin, Andrew V and Hautaniemi, Sampsa and Wu, Jianmin},
doi = {10.1093/nar/gkr967},
issn = {1362-4962},
journal = {Nucleic acids research},
keywords = {interactome},
mendeley-tags = {interactome},
month = {jan},
number = {Database issue},
pages = {D862--5},
pmid = {22067443},
title = {{PINA v2.0: mining interactome modules.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22067443 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3244997},
volume = {40},
year = {2012}
}
@article{Das2012,
abstract = {BACKGROUND A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses. RESULTS We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders. CONCLUSIONS HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.},
author = {Das, Jishnu and Yu, Haiyuan},
doi = {10.1186/1752-0509-6-92},
file = {:Users/italodovalle/Documents/MendeleyDesktop/Das, Yu/2012/BMC Systems Biology/Das, Yu{\_}2012.pdf:pdf;:Users/italodovalle/Documents/MendeleyDesktop/Das, Yu/2012/BMC Systems Biology/Das, Yu{\_}2012(2).pdf:pdf},
issn = {17520509},
journal = {BMC Systems Biology},
keywords = {Disease,Interactomes,Networks,Protein-protein interactions},
title = {{HINT: High-quality protein interactomes and their applications in understanding human disease}},
volume = {6},
year = {2012}
}

@article{DeDomenico2017,
    title = {{Multilayer modeling and analysis of human brain networks}},
    year = {2017},
    journal = {GigaScience},
    author = {De Domenico, Manlio},
    number = {5},
    month = {5},
    volume = {6},
    publisher = {Oxford University Press},
    url = {https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/gix004/2968355},
    doi = {10.1093/gigascience/gix004},
    issn = {2047-217X}
}

@article{li2016,
	title = {A scored human protein{\textendash}protein interaction network to catalyze genomic interpretation},
	author = {{Li}, {Taibo} and {Wernersson}, {Rasmus} and {Hansen}, {Rasmus B} and {Horn}, {Heiko} and {Mercer}, {Johnathan} and {Slodkowicz}, {Greg} and {Workman}, {Christopher T} and {Rigina}, {Olga} and {Rapacki}, {Kristoffer} and {Stærfeldt}, {Hans H} and {Brunak}, {Søren} and {Jensen}, {Thomas S} and {Lage}, {Kasper}},
	year = {2016},
	month = {11},
	date = {2016-11-28},
	journal = {Nature Methods},
	pages = {61--64},
	volume = {14},
	number = {1},
	doi = {10.1038/nmeth.4083},
	url = {http://dx.doi.org/10.1038/nmeth.4083},
	langid = {en}
}

@article{wan2015,
	title = {Panorama of ancient metazoan macromolecular complexes},
	author = {{Wan}, {Cuihong} and {Borgeson}, {Blake} and {Phanse}, {Sadhna} and {Tu}, {Fan} and {Drew}, {Kevin} and {Clark}, {Greg} and {Xiong}, {Xuejian} and {Kagan}, {Olga} and {Kwan}, {Julian} and {Bezginov}, {Alexandr} and {Chessman}, {Kyle} and {Pal}, {Swati} and {Cromar}, {Graham} and {Papoulas}, {Ophelia} and {Ni}, {Zuyao} and {Boutz}, {Daniel R.} and {Stoilova}, {Snejana} and {Havugimana}, {Pierre C.} and {Guo}, {Xinghua} and {Malty}, {Ramy H.} and {Sarov}, {Mihail} and {Greenblatt}, {Jack} and {Babu}, {Mohan} and {Derry}, {W. Brent} and {R. Tillier}, {Elisabeth} and {Wallingford}, {John B.} and {Parkinson}, {John} and {Marcotte}, {Edward M.} and {Emili}, {Andrew}},
	year = {2015},
	month = {09},
	date = {2015-09},
	journal = {Nature},
	pages = {339--344},
	volume = {525},
	number = {7569},
	doi = {10.1038/nature14877},
	url = {http://dx.doi.org/10.1038/nature14877},
	langid = {en}
}

@article{cheng2014,
	title = {Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy},
	author = {{Cheng}, {Feixiong} and {Jia}, {Peilin} and {Wang}, {Quan} and {Zhao}, {Zhongming}},
	year = {2014},
	month = {05},
	date = {2014-05-18},
	journal = {Oncotarget},
	pages = {3697--3710},
	volume = {5},
	number = {11},
	doi = {10.18632/oncotarget.1984},
	url = {http://dx.doi.org/10.18632/oncotarget.1984},
	langid = {en}
}

@article{wishart2017,
	title = {DrugBank 5.0: a major update to the DrugBank database for 2018},
	author = {{Wishart}, {David S} and {Feunang}, {Yannick D} and {Guo}, {An C} and {Lo}, {Elvis J} and {Marcu}, {Ana} and {Grant}, {Jason R} and {Sajed}, {Tanvir} and {Johnson}, {Daniel} and {Li}, {Carin} and {Sayeeda}, {Zinat} and {Assempour}, {Nazanin} and {Iynkkaran}, {Ithayavani} and {Liu}, {Yifeng} and {Maciejewski}, {Adam} and {Gale}, {Nicola} and {Wilson}, {Alex} and {Chin}, {Lucy} and {Cummings}, {Ryan} and {Le}, {Diana} and {Pon}, {Allison} and {Knox}, {Craig} and {Wilson}, {Michael}},
	year = {2017},
	month = {11},
	date = {2017-11-08},
	journal = {Nucleic Acids Research},
	pages = {D1074--D1082},
	volume = {46},
	number = {D1},
	doi = {10.1093/nar/gkx1037},
	url = {http://dx.doi.org/10.1093/nar/gkx1037},
	langid = {en}
}

@article{davis2020,
	title = {Comparative Toxicogenomics Database (CTD): update 2021},
	author = {{Davis}, {Allan Peter} and {Grondin}, {Cynthia J} and {Johnson}, {Robin J} and {Sciaky}, {Daniela} and {Wiegers}, {Jolene} and {Wiegers}, {Thomas C} and {Mattingly}, {Carolyn J}},
	year = {2020},
	month = {10},
	date = {2020-10-17},
	journal = {Nucleic Acids Research},
	pages = {D1138--D1143},
	volume = {49},
	number = {D1},
	doi = {10.1093/nar/gkaa891},
	url = {http://dx.doi.org/10.1093/nar/gkaa891},
	langid = {en}
}

@article{corsello2017,
	title = {The Drug Repurposing Hub: a next-generation drug library and information resource},
	author = {{Corsello}, {Steven M} and {Bittker}, {Joshua A} and {Liu}, {Zihan} and {Gould}, {Joshua} and {McCarren}, {Patrick} and {Hirschman}, {Jodi E} and {Johnston}, {Stephen E} and {Vrcic}, {Anita} and {Wong}, {Bang} and {Khan}, {Mariya} and {Asiedu}, {Jacob} and {Narayan}, {Rajiv} and {Mader}, {Christopher C} and {Subramanian}, {Aravind} and {Golub}, {Todd R}},
	year = {2017},
	month = {04},
	date = {2017-04},
	journal = {Nature Medicine},
	pages = {405--408},
	volume = {23},
	number = {4},
	doi = {10.1038/nm.4306},
	url = {http://dx.doi.org/10.1038/nm.4306},
	langid = {en}
}
@article{Fazekas2013,
abstract = {BACKGROUND Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. DESCRIPTION We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. CONCLUSIONS With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.},
author = {Fazekas, D{\'{a}}vid and Koltai, Mih{\'{a}}ly and T{\"{u}}rei, D{\'{e}}nes and M{\'{o}}dos, Dezső and P{\'{a}}lfy, M{\'{a}}t{\'{e}} and D{\'{u}}l, Zolt{\'{a}}n and Zs{\'{a}}kai, Lilian and Szalay-Bekő, M{\'{a}}t{\'{e}} and Lenti, Katalin and Farkas, Ill{\'{e}}s J and Vellai, Tibor and Csermely, P{\'{e}}ter and Korcsm{\'{a}}ros, Tam{\'{a}}s},
doi = {10.1186/1752-0509-7-7},
issn = {1752-0509},
journal = {BMC systems biology},
keywords = {interactome},
mendeley-tags = {interactome},
month = {jan},
pages = {7},
pmid = {23331499},
title = {{SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23331499 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3599410},
volume = {7},
year = {2013}
}
@misc{Gashaw2011,
abstract = {Novel therapeutics in areas with a high unmet medical need are based on innovative drug targets. Although 'biologicals' have enlarged the space of druggable molecules, the number of appropriate drug targets is still limited. Discovering and assessing the potential therapeutic benefit of a drug target is based not only on experimental, mechanistic and pharmacological studies but also on a theoretical molecular druggability assessment, an early evaluation of potential side effects and considerations regarding opportunities for commercialization. This article defines key properties of a good drug target from the perspective of a pharmaceutical company. {\textcopyright} 2011 Elsevier Ltd. All rights reserved.},
author = {Gashaw, Isabella and Ellinghaus, Peter and Sommer, Anette and Asadullah, Khusru},
booktitle = {Drug Discovery Today},
doi = {10.1016/j.drudis.2011.09.007},
file = {:Users/deisygysi/Library/Application Support/Mendeley Desktop/Downloaded/Gashaw et al. - 2011 - What makes a good drug target.pdf:pdf},
issn = {13596446},
month = {dec},
number = {23-24},
pages = {1037--1043},
pmid = {21945861},
title = {{What makes a good drug target?}},
url = {www.drugdiscoverytoday.com},
volume = {16},
year = {2011}
}
@article{Guney2016,
    title = {{Network-based in silico drug efficacy screening}},
    year = {2016},
    journal = {Nature Communications},
    author = {Guney, Emre and Menche, Jörg and Vidal, Marc and Bar{\'{a}}basi, Albert-László László},
    number = {1},
    month = {2},
    pages = {10331},
    volume = {7},
    publisher = {Nature Publishing Group},
    url = {http://www.nature.com/articles/ncomms10331},
    doi = {10.1038/ncomms10331},
    issn = {20411723},
    keywords = {Biochemical networks, Bioinformatics, Clinical pharmacology, Medical research}
}
@article{Gysi2020a,
abstract = {The COVID-19 pandemic demands the rapid identification of drug-repurpusing candidates  In the past decade, network medicine had developed a framework consisting of a series of quantitative approaches and predictive tools to study host-pathogen interactions, unveil the molecular mechanisms of the infection, identify comorbidities as well as rapidly detect drug repurpusing candidates  Here, we adapt the network-based toolset to COVID-19, recovering the primary pulmonary manifestations of the virus in the lung as well as observed comorbidities associated with cardiovascular diseases  We predict that the virus can manifest itself in other tissues, such as the reproductive system, and brain regions, moreover we predict neurological comorbidities  We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates  We validate the accuracy of our predictions using drugs currently in clinical trials, and an expression-based validation of selected candidates suggests that these drugs, with known toxicities and side effects, could be moved to clinical trials rapidly},
author = {Gysi, D M and {Do Valle  Zitnik, M.}, {\'{I}} and Ameli, A and Gan, X and Varol, O and Sanchez, H and Baron, R M and Ghiassian, D and Loscalzo, J and Barab{\'{a}}si, A L},
journal = {ArXiv},
title = {{Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19}},
year = {2020}
}
@article{Gysi2020,
abstract = {Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.},
author = {Gysi, Deisy Morselli and Nowick, Katja},
doi = {10.1098/rsif.2019.0610},
issn = {17425662},
journal = {Journal of the Royal Society, Interface},
keywords = {construction of networks,network comparison,network evolution,networks,networks in life sciences},
month = {may},
number = {166},
pages = {20190610},
pmid = {32370689},
title = {{Construction, comparison and evolution of networks in life sciences and other disciplines}},
url = {https://royalsocietypublishing.org/doi/10.1098/rsif.2019.0610},
volume = {17},
year = {2020}
}

@article{Hein2015,
    title = {{A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances}},
    year = {2015},
    journal = {Cell},
    author = {Hein, Marco Y. and Hubner, Nina C. and Poser, Ina and Cox, Jürgen and Nagaraj, Nagarjuna and Toyoda, Yusuke and Gak, Igor A. and Weisswange, Ina and Mansfeld, Jörg and Buchholz, Frank and Hyman, Anthony A. and Mann, Matthias},
    number = {3},
    month = {10},
    pages = {712--723},
    volume = {163},
    publisher = {Cell Press},
    doi = {10.1016/j.cell.2015.09.053},
    issn = {10974172},
    pmid = {26496610}
}
@misc{Hobert2008GeneMicroRNAs,
    title = {{Gene regulation by transcription factors and MicroRNAs}},
    year = {2008},
    booktitle = {Science},
    author = {Hobert, Oliver},
    number = {5871},
    month = {3},
    pages = {1785--1786},
    volume = {319},
    publisher = {American Association for the Advancement of Science},
    doi = {10.1126/science.1151651},
    issn = {00368075}
}


@article{Hornbeck2015,
abstract = {PhosphoSitePlus({\textregistered}) (PSP, http://www.phosphosite.org/), a knowledgebase dedicated to mammalian post-translational modifications (PTMs), contains over 330,000 non-redundant PTMs, including phospho, acetyl, ubiquityl and methyl groups. Over 95{\%} of the sites are from mass spectrometry (MS) experiments. In order to improve data reliability, early MS data have been reanalyzed, applying a common standard of analysis across over 1,000,000 spectra. Site assignments with P {\textgreater} 0.05 were filtered out. Two new downloads are available from PSP. The 'Regulatory sites' dataset includes curated information about modification sites that regulate downstream cellular processes, molecular functions and protein-protein interactions. The 'PTMVar' dataset, an intersect of missense mutations and PTMs from PSP, identifies over 25,000 PTMVars (PTMs Impacted by Variants) that can rewire signaling pathways. The PTMVar data include missense mutations from UniPROTKB, TCGA and other sources that cause over 2000 diseases or syndromes (MIM) and polymorphisms, or are associated with hundreds of cancers. PTMVars include 18 548 phosphorlyation sites, 3412 ubiquitylation sites, 2316 acetylation sites, 685 methylation sites and 245 succinylation sites.},
author = {Hornbeck, Peter V and Zhang, Bin and Murray, Beth and Kornhauser, Jon M and Latham, Vaughan and Skrzypek, Elzbieta},
doi = {10.1093/nar/gku1267},
issn = {1362-4962},
journal = {Nucleic acids research},
keywords = {interactome},
mendeley-tags = {interactome},
month = {jan},
number = {Database issue},
pages = {D512--20},
pmid = {25514926},
title = {{PhosphoSitePlus, 2014: mutations, PTMs and recalibrations.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25514926 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4383998},
volume = {43},
year = {2015}
}

@article{Huttlin2017,
    title = {{Architecture of the human interactome defines protein communities and disease networks}},
    year = {2017},
    journal = {Nature},
    author = {Huttlin, Edward L. and Bruckner, Raphael J. and Paulo, Joao A. and Cannon, Joe R. and Ting, Lily and Baltier, Kurt and Colby, Greg and Gebreab, Fana and Gygi, Melanie P. and Parzen, Hannah and Szpyt, John and Tam, Stanley and Zarraga, Gabriela and Pontano-Vaites, Laura and Swarup, Sharan and White, Anne E. and Schweppe, Devin K. and Rad, Ramin and Erickson, Brian K. and Obar, Robert A. and Guruharsha, K. G. and Li, Kejie and Artavanis-Tsakonas, Spyros and Gygi, Steven P. and Wade Harper, J.},
    number = {7655},
    month = {5},
    pages = {505--509},
    volume = {545},
    publisher = {Nature Publishing Group},
    doi = {10.1038/nature22366},
    issn = {14764687},
    pmid = {28514442}
}
@article{Kempa2019HighAnalysis,
    title = {{High throughput screening of complex biological samples with mass spectrometry-from bulk measurements to single cell analysis}},
    year = {2019},
    journal = {Analyst},
    author = {Kempa, Emily E. and Hollywood, Katherine A. and Smith, Clive A. and Barran, Perdita E.},
    number = {3},
    month = {2},
    pages = {872--891},
    volume = {144},
    publisher = {Royal Society of Chemistry},
    doi = {10.1039/c8an01448e},
    issn = {13645528}
}


@article{Kurant2006LayeredNetworks,
    title = {{Layered Complex Networks}},
    year = {2006},
    journal = {Physical Review Letters},
    author = {Kurant, Maciej and Thiran, Patrick},
    number = {13},
    month = {4},
    pages = {138701},
    volume = {96},
    publisher = {American Physical Society},
    url = {https://link.aps.org/doi/10.1103/PhysRevLett.96.138701},
    doi = {10.1103/PhysRevLett.96.138701},
    issn = {0031-9007}
}


@article{Kivela2014MultilayerNetworks,
    title = {{Multilayer networks}},
    year = {2014},
    journal = {Journal of Complex Networks},
    author = {Kivela, M. and Arenas, A. and Barthelemy, M. and Gleeson, J. P. and Moreno, Y. and Porter, M. A.},
    number = {3},
    month = {9},
    pages = {203--271},
    volume = {2},
    publisher = {Oxford University Press},
    url = {https://academic.oup.com/comnet/article-lookup/doi/10.1093/comnet/cnu016},
    doi = {10.1093/comnet/cnu016},
    issn = {2051-1310}
}

@article{Latchman1997TranscriptionOverview,
    title = {{Transcription factors: An overview}},
    year = {1997},
    journal = {International Journal of Biochemistry and Cell Biology},
    author = {Latchman, D. S.},
    number = {12},
    pages = {1305--1312},
    volume = {29},
    publisher = {Elsevier Ltd},
    doi = {10.1016/S1357-2725(97)00085-X},
    issn = {13572725},
    pmid = {9570129},
    keywords = {Gene regulation, Transcription factors}
}

@article{Menche2015,
    title = {{Uncovering disease-disease relationships through the incomplete interactome}},
    year = {2015},
    journal = {Science},
    author = {Menche, Jörg and Sharma, Amitabh and Kitsak, Maksim and Ghiassian, Susan Dina and Vidal, Marc and Loscalzo, Joseph and Barabasi, Albert-Laszlo},
    number = {6224},
    month = {5},
    volume = {347},
    publisher = {American Association for the Advancement of Science},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/11988575},
    doi = {10.1126/science.1065103},
    issn = {00368075},
    pmid = {11988575}
}
@article{Meyer2013,
abstract = {UNLABELLED INstruct is a database of high-quality, 3D, structurally resolved protein interactome networks in human and six model organisms. INstruct combines the scale of available high-quality binary protein interaction data with the specificity of atomic-resolution structural information derived from co-crystal evidence using a tested interaction interface inference method. Its web interface is designed to allow for flexible search based on standard and organism-specific protein and gene-naming conventions, visualization of protein architecture highlighting interaction interfaces and viewing and downloading custom 3D structurally resolved interactome datasets. AVAILABILITY INstruct is freely available on the web at http://instruct.yulab.org with all major browsers supported.},
author = {Meyer, Michael J and Das, Jishnu and Wang, Xiujuan and Yu, Haiyuan},
doi = {10.1093/bioinformatics/btt181},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {interactome},
mendeley-tags = {interactome},
month = {jun},
number = {12},
pages = {1577--9},
pmid = {23599502},
title = {{INstruct: a database of high-quality 3D structurally resolved protein interactome networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23599502 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3673217},
volume = {29},
year = {2013}
}

@article{Meyer2018,
abstract = {We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.},
author = {Meyer, Michael J and Beltr{\'{a}}n, Juan Felipe and Liang, Siqi and Fragoza, Robert and Rumack, Aaron and Liang, Jin and Wei, Xiaomu and Yu, Haiyuan},
doi = {10.1038/nmeth.4540},
issn = {1548-7105},
journal = {Nature methods},
keywords = {interactome},
mendeley-tags = {interactome},
number = {2},
pages = {107--114},
pmid = {29355848},
title = {{Interactome INSIDER: a structural interactome browser for genomic studies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/29355848 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6026581},
volume = {15},
year = {2018}
}


@book{Newman2018,
abstract = {Second edition. The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract new knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and `developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models; and models of processes taking place on networks. 1. Introduction -- 2. Technological networks -- 3. Networks of information -- 4. Social networks -- 5. Biological networks -- 6. Mathematics of networks -- 7. Measures and metrics -- 8. Computer algorithms -- 9. Network statistics and mesurement error -- 10. The structure of real-world networks -- 11. Random graphs -- 12. The configuration model -- 13. Models of network formation -- 14. Community structure -- 15. Percolation and network resilience -- 16. Epidemics on networks -- 17. Dynamical systems on networks -- 18. Network search -- References -- Index.},
author = {Newman, Mark E J},
year = {2018},
isbn = {9780192527493},
title = {{Networks}},
url = {https://books.google.com.br/books?hl=en{\&}lr={\&}id=YdZjDwAAQBAJ{\&}oi=fnd{\&}pg=PP1{\&}dq=networks+an+introduction{\&}ots=V{\_}J-6Ig5uu{\&}sig=wlDx19lsqamr-20U5ZUbEjwW3uM{\#}v=onepage{\&}q=networks an introduction{\&}f=false https://www.ebooks.com/96308191/networks/newman-mark/},
}

@article{barabasi2004network,
    title = {{Network biology: Understanding the cell's functional organization}},
    year = {2004},
    journal = {Nature Reviews Genetics},
    author = {Barab{\'{a}}si, Albert László and Oltvai, Zoltán N.},
    number = {2},
    month = {2},
    pages = {101--113},
    volume = {5},
    publisher = {Nature Publishing Group},
    url = {http://www.nature.com/articles/nrg1272},
    isbn = {1471-0056 (Print) 1471-0056 (Linking)},
    doi = {10.1038/nrg1272},
    issn = {14710056},
    pmid = {14735121},
    arxivId = {NIHMS150003}
}

@article{Barabasi2007,
    title = {{Network medicine - From obesity to the "Diseasome"}},
    year = {2007},
    journal = {New England Journal of Medicine},
    author = {Barab{\'{a}}si, Albert László},
    doi = {10.1056/NEJMe078114},
    issn = {15334406}
}

@article{Licata2012,
author = {Licata, Luana and Briganti, Leonardo and Peluso, Daniele and Perfetto, Livia and Iannuccelli, Marta and Galeota, Eugenia and Sacco, Francesca and Palma, Anita and Nardozza, Aurelio Pio and Santonico, Elena and Castagnoli, Luisa and Cesareni, Gianni},
doi = {10.1093/nar/gkr930},
issn = {1362-4962},
journal = {Nucleic Acids Research},
keywords = {interactome},
mendeley-tags = {interactome},
month = {jan},
number = {D1},
pages = {D857--D861},
title = {{MINT, the molecular interaction database: 2012 update}},
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkr930},
volume = {40},
year = {2012}
}


@article{Luck2020,
abstract = {Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all' reference interactome map of human binary protein interactions, or ‘HuRI'. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.},
author = {Luck, Katja and Kim, Dae Kyum and Lambourne, Luke and Spirohn, Kerstin and Begg, Bridget E. and Bian, Wenting and Brignall, Ruth and Cafarelli, Tiziana and Campos-Laborie, Francisco J. and Charloteaux, Benoit and Choi, Dongsic and Cot{\'{e}}, Atina G. and Daley, Meaghan and Deimling, Steven and Desbuleux, Alice and Dricot, Am{\'{e}}lie and Gebbia, Marinella and Hardy, Madeleine F. and Kishore, Nishka and Knapp, Jennifer J. and Kov{\'{a}}cs, Istv{\'{a}}n A. and Lemmens, Irma and Mee, Miles W. and Mellor, Joseph C. and Pollis, Carl and Pons, Carles and Richardson, Aaron D. and Schlabach, Sadie and Teeking, Bridget and Yadav, Anupama and Babor, Mariana and Balcha, Dawit and Basha, Omer and Bowman-Colin, Christian and Chin, Suet Feung and Choi, Soon Gang and Colabella, Claudia and Coppin, Georges and D'Amata, Cassandra and {De Ridder}, David and {De Rouck}, Steffi and Duran-Frigola, Miquel and Ennajdaoui, Hanane and Goebels, Florian and Goehring, Liana and Gopal, Anjali and Haddad, Ghazal and Hatchi, Elodie and Helmy, Mohamed and Jacob, Yves and Kassa, Yoseph and Landini, Serena and Li, Roujia and van Lieshout, Natascha and MacWilliams, Andrew and Markey, Dylan and Paulson, Joseph N. and Rangarajan, Sudharshan and Rasla, John and Rayhan, Ashyad and Rolland, Thomas and San-Miguel, Adriana and Shen, Yun and Sheykhkarimli, Dayag and Sheynkman, Gloria M. and Simonovsky, Eyal and Taşan, Murat and Tejeda, Alexander and Tropepe, Vincent and Twizere, Jean Claude and Wang, Yang and Weatheritt, Robert J. and Weile, Jochen and Xia, Yu and Yang, Xinping and Yeger-Lotem, Esti and Zhong, Quan and Aloy, Patrick and Bader, Gary D. and {De Las Rivas}, Javier and Gaudet, Suzanne and Hao, Tong and Rak, Janusz and Tavernier, Jan and Hill, David E. and Vidal, Marc and Roth, Frederick P. and Calderwood, Michael A.},
doi = {10.1038/s41586-020-2188-x},
issn = {14764687},
journal = {Nature},
pmid = {32296183},
title = {{A reference map of the human binary protein interactome}},
year = {2020}
}
@article{MattickNon-codingRNA,
abstract = {The term non-coding RNA (ncRNA) is commonly employed for RNA that does not encode a protein, but this does not mean that such RNAs do not contain information nor have function. Although it has been generally assumed that most genetic information is transacted by proteins, recent evidence suggests that the majority of the genomes of mammals and other complex organisms is in fact transcribed into ncRNAs, many of which are alternatively spliced and/or processed into smaller products. These ncRNAs include microRNAs and snoRNAs (many if not most of which remain to be identified), as well as likely other classes of yet-to-be-discovered small regulatory RNAs, and tens of thousands of longer transcripts (including complex patterns of interlacing and overlapping sense and antisense transcripts), most of whose functions are unknown. These RNAs (including those derived from introns) appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development, including chromatin architecture/epigenetic memory, transcription, RNA splicing, editing, translation and turnover. RNA regulatory networks may determine most of our complex characteristics, play a significant role in disease and constitute an unexplored world of genetic variation both within and between species.},
author = {Mattick, John S and Makunin, Igor V},
doi = {10.1093/hmg/ddl046},
file = {:Users/deisygysi/Library/Application Support/Mendeley Desktop/Downloaded/Mattick, Makunin - Unknown - Non-coding RNA.pdf:pdf},
issn = {1460-2083},
journal = {Human Molecular Genetics},
month = {apr},
number = {suppl{\_}1},
pages = {R17--R29},
pmid = {16651366},
title = {{Non-coding RNA}},
url = {https://academic.oup.com/hmg/article-abstract/15/suppl{\_}1/R17/632705 http://academic.oup.com/hmg/article/15/suppl{\_}1/R17/632705/Noncoding-RNA},
volume = {15},
year = {2006}
}


@article{Mosca2013,
abstract = {Network-centered approaches are increasingly used to understand the fundamentals of biology. However, the molecular details contained in the interaction networks, often necessary to understand cellular processes, are very limited, and the experimental difficulties surrounding the determination of protein complex structures make computational modeling techniques paramount. Here we present Interactome3D, a resource for the structural annotation and modeling of protein-protein interactions. Through the integration of interaction data from the main pathway repositories, we provide structural details at atomic resolution for over 12,000 protein-protein interactions in eight model organisms. Unlike static databases, Interactome3D also allows biologists to upload newly discovered interactions and pathways in any species, select the best combination of structural templates and build three-dimensional models in a fully automated manner. Finally, we illustrate the value of Interactome3D through the structural annotation of the complement cascade pathway, rationalizing a potential common mechanism of action suggested for several disease-causing mutations.},
author = {Mosca, Roberto and C{\'{e}}ol, Arnaud and Aloy, Patrick},
doi = {10.1038/nmeth.2289},
issn = {1548-7105},
journal = {Nature methods},
keywords = {interactome,interactome3d},
mendeley-tags = {interactome,interactome3d},
month = {jan},
number = {1},
pages = {47--53},
pmid = {23399932},
title = {{Interactome3D: adding structural details to protein networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23399932},
volume = {10},
year = {2013}
}

@article{Mucha2010CommunityNetworks,
    title = {{Community structure in time-dependent, multiscale, and multiplex networks.}},
    year = {2010},
    journal = {Science (New York, N.Y.)},
    author = {Mucha, Peter J and Richardson, Thomas and Macon, Kevin and Porter, Mason A and Onnela, Jukka-Pekka},
    number = {5980},
    month = {5},
    pages = {876--8},
    volume = {328},
    publisher = {American Association for the Advancement of Science},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/20466926},
    doi = {10.1126/science.1184819},
    issn = {1095-9203},
    pmid = {20466926}
}
@misc{Parrish2006YeastMapping,
    title = {{Yeast two-hybrid contributions to interactome mapping}},
    year = {2006},
    booktitle = {Current Opinion in Biotechnology},
    author = {Parrish, Jodi R. and Gulyas, Keith D. and Finley, Russell L.},
    number = {4},
    month = {8},
    pages = {387--393},
    volume = {17},
    publisher = {Elsevier Current Trends},
    doi = {10.1016/j.copbio.2006.06.006},
    issn = {09581669}
}
@incollection{Slack2013,
    title = {{Molecular Biology of the Cell}},
    year = {2013},
    booktitle = {Principles of Tissue Engineering: Fourth Edition},
    author = {Slack, J. M.W.},
    isbn = {9780123983589},
    doi = {10.1016/B978-0-12-398358-9.00007-0},
    issn = {0044-0086},
    keywords = {Cell, Cytoskeleton, Extracellular matrix, Gene, Metabolism, Plasma membrane}
}


@article{Radicchi2013AbruptNetworks,
    title = {{Abrupt transition in the structural formation of interconnected networks}},
    year = {2013},
    journal = {Nature Physics},
    author = {Radicchi, Filippo and Arenas, Alex},
    number = {11},
    month = {11},
    pages = {717--720},
    volume = {9},
    publisher = {Nature Publishing Group},
    url = {http://www.nature.com/articles/nphys2761},
    doi = {10.1038/nphys2761},
    issn = {1745-2473},
    keywords = {Complex networks, Phase transitions and critical phenomena}
}

@article{barabasi1999emergence,
    title = {{Emergence of scaling in random networks}},
    year = {1999},
    journal = {Science},
    author = {Barab{\'{a}}si, Albert László and Albert, Réka},
    number = {5439},
    pages = {509--512},
    volume = {286},
    publisher = {American Association for the Advancement of Science},
    doi = {10.1126/science.286.5439.509},
    issn = {00368075},
    pmid = {10521342},
    arxivId = {cond-mat/9910332}
}


@article{Vazquez2003GrowingCorrelations,
    title = {{Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations}},
    year = {2003},
    journal = {Physical Review E},
    author = {V{\'{a}}zquez, Alexei},
    number = {5},
    month = {5},
    pages = {056104},
    volume = {67},
    publisher = {American Physical Society},
    url = {https://link.aps.org/doi/10.1103/PhysRevE.67.056104},
    doi = {10.1103/PhysRevE.67.056104},
    issn = {1063-651X}
}


@incollection{Tanase2012MicroRNAs,
    title = {{MicroRNAs}},
    year = {2012},
    booktitle = {Molecular Pathology of Pituitary Adenomas},
    author = {Tanase, Cristiana Pistol and Ogrezeanu, Irina and Badiu, Corin},
    month = {1},
    pages = {91--96},
    publisher = {Elsevier},
    doi = {10.1016/b978-0-12-415830-6.00008-1}
}

@misc{Metzker2010SequencingGeneration,
    title = {{Sequencing technologies the next generation}},
    year = {2010},
    booktitle = {Nature Reviews Genetics},
    author = {Metzker, Michael L.},
    number = {1},
    month = {1},
    pages = {31--46},
    volume = {11},
    publisher = {Nature Publishing Group},
    doi = {10.1038/nrg2626},
    issn = {14710056},
    pmid = {19997069},
    keywords = {Agriculture, Animal Genetics and Genomics, Biomedicine, Cancer Research, Gene Function, Human Genetics, general}
}

@misc{Park2009ChIP-seq:Technology,
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}
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abstract = {DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains {\textgreater}4100 drug entries including {\textgreater}800 FDA approved small molecule and biotech drugs as well as {\textgreater}3200 experimental drugs. Additionally, {\textgreater}14,000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains {\textgreater}80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at http://redpoll.pharmacy.ualberta.ca/drugbank/.},
author = {Wishart, David S and Knox, Craig and Guo, An Chi and Shrivastava, Savita and Hassanali, Murtaza and Stothard, Paul and Chang, Zhan and Woolsey, Jennifer},
doi = {10.1093/nar/gkj067},
issn = {1362-4962},
journal = {Nucleic acids research},
month = {jan},
number = {Database issue},
pages = {D668--72},
pmid = {16381955},
title = {{DrugBank: a comprehensive resource for in silico drug discovery and exploration.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16381955 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1347430},
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year = {2006}
}

@inproceedings{Zitnik2018,
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    arxivId = {1802.00543}
}

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