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data/raw.csv

@@ -106,7 +106,7 @@ Deep learning for the dynamic prediction of multivariate longitudinal and surviv
 parametrization not clearly defined, from Figure 5 looks like they predict the (conditional) PMF directly. Similar to deephit.",,,,,,,,
 Deep Cox Mixtures for Survival Regression,https://proceedings.mlr.press/v149/nagpal21a,C. Nagpal et al.,2021,DCM,Cox-based,hazard rate,yes,???,VAE,no,no,no,no,no,no,no,no,no,no,"https://github.com/chiragnagpal/deep_cox_mixtures, https://autonlab.org/auton-survival","FLCHAIN, SEER, SUPPORT",6524 (FLCHAIN),no,no,no,PASS,,(extended + modified),,,,,,,,
 Improved survival analysis by learning shared genomic information from pan-cancer data,https://doi.org/10.1093/bioinformatics/btaa462,S. Kim et al.,2020,VAECox,Cox-based,hazard rate,yes,???,VAE + transfer learning,no,no,no,no,no,no,no,yes,no,no,https://github.com/SunkyuKim/VAECox,"TCGA (BLCA, BRCA, HNSC, KIRC, LGG, LIHC, LUAD, LUSC, OV, STAD, CESC, COAD, GBM, KIRP, LAML, PAAD, PRAD, SKCM, THCA, UCEC)",9127 (combined for pre-training),no,no,no,PASS,,multiple losses b/c RMSE + KLD loss for VAE part (pretraining/encoding) and Cox loss for survival part (fine-tuning),,,,,,,,
-SAVAE: Leveraging the variational Bayes autoencoder for survival analysis,https://arxiv.org/pdf/2312.14651.pdf,P. Apellániz et al.,2023,SAVAE,parametric,parameters of differentiable time distribution,yes,no,VAE,no,no,no,no,no,no,no,no,no,BP,https://github.com/Patricia-A-Apellaniz/savae," FLCHAIN, GBSG, METABRIC, NWTCO, PBC, PNEUMON, STD, SUPPORT, WHAS",418 (PBC),no,no,no,PASS,,could be applied to high dim; could be extended to multimodality due to using the VAE architecture,,,,,,,,"could be applied to high dim, could be extended to multimodality due to using the VAE architecture"
+SAVAE: Leveraging the variational Bayes autoencoder for survival analysis,https://doi.org/10.1038/s41598-024-76047-z,P. Apellániz et al.,2023,SAVAE,parametric,parameters of differentiable time distribution,yes,no,VAE,no,no,no,no,no,no,no,no,no,BP,https://github.com/Patricia-A-Apellaniz/savae," FLCHAIN, GBSG, METABRIC, NWTCO, PBC, PNEUMON, STD, SUPPORT, WHAS",418 (PBC),no,no,no,PASS,,could be applied to high dim; could be extended to multimodality due to using the VAE architecture,,,,,,,,"could be applied to high dim, could be extended to multimodality due to using the VAE architecture"
 A Hierarchical Approach to Multi-Event Survival Analysis,https://doi.org/10.1609/aaai.v35i1.16138,D. Tjandra et al.,2021,Tja2021,???,??? discrete-time,yes,no,FFNN (hierarchical),no,no,??? yes,??? yes,no,no,no,no,no,no,https://gitlab.eecs.umich.edu/mld3/hierarchicalsurvival-analysis,"ADNI, MIMIC-III, SEER",1604 (ADNI),yes,no,no,PASS,,,,,,,,,,
 Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network,https://doi.org/10.1007/978-3-031-47076-9_12,Z. Li et al.,2023,CCL-GAT,Cox-based,hazard rate,no,no,Graph Attention Network,no,no,no,no,no,no,no,no,yes,integrated gradients,no,TCGA (STAD),309,no,no,no,PASS,,,,,,,,,,
 AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images,https://doi.org/10.1016/j.media.2023.103020,P. Liu et al.,2024,AdvMIL,GAN-based,custom loss,yes,no,GAN,no,no,no,no,no,no,no,no,yes,no,https://github.com/liupei101/AdvMIL,"NLST, TCGA (BRCA, LGG)",447 (NLST),no,no,yes,PASS,,,,,,,,,,