iDNA-ABF-automl

This repository is a nni version and base on pytorchlighting to the model iDNA-ABF

**We do not use adversarial training in this repository, so the results may be little lower than the metrics in the paper, but they are still higher than the other methods. ** It just make easy for you to reproduce some results, and we haven't adjust parameters carefully. If you want the original parameters, please send email to me and I will give a version to you.

Now, We have provided a base parameters by One drive, you can download by this One drive share

How to use

main

• train_ABF.py -> train and test model
• train_model.py

nni

• fusion: nnictl create -p 9990 -c config_idna.yml
• bert: nnictl create -p 9990 -c config_bert.yml
• searh_space_idna.json[fusion]
• searh_space_bert.json[bert]

module

• data_module: onehot: (1) true -> auto tokenlize (2) false -> input directly
• lightning_module: add models

The pytorchlighting parameters and results

5hmC_H.sapiens (fMkaF)

{
    "batch_size": 64,
    "lr": 0.00005,
    "dropout": 0.7,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.950512	0.971124	0.902456	0.951867	0.967769	0.973189
Q	SE	SP	PPV	NPV
0.950512	0.978669	0.922355	0.926494	0.977396

5hmC_M.musculus (fMkaF)

{
    "batch_size": 16,
    "lr": 0.0001,
    "dropout": 0.5,
    "alpha": [
        0.2,
        0.8
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.967645	0.976875	0.935292	0.967672	0.968145	0.968303
Q	SE	SP	PPV	NPV
0.967645	0.968461	0.96683	0.966884	0.96841

4mC_C.equisetifolia (ie9Je)

{
    "batch_size": 128,
    "lr": 0.0005,
    "dropout": 0.7,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.846995	0.902177	0.698171	0.83815	0.810056	0.801105
Q	SE	SP	PPV	NPV
0.846995	0.79235	0.901639	0.889571	0.812808

4mC_F.vesca (X8Q6Y)

{
    "batch_size": 32,
    "lr": 0.0001,
    "dropout": 0.4,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.851291	0.923053	0.70327	0.854506	0.865735	0.869543
Q	SE	SP	PPV	NPV
0.851291	0.873386	0.829197	0.836425	0.867532

4mC_S.cerevisiae (MRjYH)

{
    "batch_size": 16,
    "lr": 0.00005,
    "dropout": 0.5,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.720425	0.775838	0.44199	0.710016	0.694501	0.68948
Q	SE	SP	PPV	NPV
0.720425	0.68453	0.75632	0.737473	0.70566

4mC_Tolypocladium (MUVm1)

{
    "batch_size": 32,
    "lr": 0.00005,
    "dropout": 0.1,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.737896	0.814666	0.475839	0.736054	0.732962	0.731937
Q	SE	SP	PPV	NPV
0.737896	0.730915	0.744878	0.741265	0.73462

6mA_A.thaliana (i1D5l)

{
    "batch_size": 64,
    "lr": 0.00005,
    "dropout": 0.5,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.858622	0.93164	0.717525	0.856616	0.849383	0.846999
Q	SE	SP	PPV	NPV
0.858622	0.844629	0.872615	0.868948	0.848859

6mA_C.elegans (YUQ7c)

{
    "batch_size": 128,
    "lr": 0.00005,
    "dropout": 0.3,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.910176	0.966053	0.820387	0.910591	0.913126	0.913974
Q	SE	SP	PPV	NPV
0.910176	0.914824	0.905528	0.906398	0.914025

6mA_C.equisetifolia (ICjHp)

{
    "batch_size": 64,
    "lr": 0.00005,
    "dropout": 0.7,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.722717	0.803041	0.44784	0.736364	0.75877	0.766545
Q	SE	SP	PPV	NPV
0.722717	0.774481	0.670953	0.906398	0.701823

6mA_D.melanogaster (ICjHp)

{
    "batch_size": 128,
    "lr": 0.0001,
    "dropout": 0.3,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.92109	0.969753	0.842273	0.920501	0.916392	0.91503
Q	SE	SP	PPV	NPV
0.92109	0.913673	0.928508	0.927431	0.914935

6mA_F.vesca (FlBH6)

{
    "batch_size": 128,
    "lr": 0.00005,
    "dropout": 0.7,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.941973	0.979585	0.883982	0.942234	0.944781	0.945633
Q	SE	SP	PPV	NPV
0.941973	0.946486	0.93746	0.938019	0.945999

6mA_H.sapiens (n7zZ9)

{
    "batch_size": 32,
    "lr": 0.0001,
    "dropout": 0.7,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.905367	0.966387	0.811073	0.903979	0.896094	0.893496
Q	SE	SP	PPV	NPV
0.905367	0.890913	0.919821	0.917434	0.893978

6mA_R.chinensis (xWuPj)

{
    "batch_size": 16,
    "lr": 0.0001,
    "dropout": 0.7,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.8628	0.94		0.87
Q	SE	SP	PPV	NPV
	0.88	0.85

Mention: some problems lead to the interrupt during the training process, this is the result before interrupt.

6mA_S.cerevisiae (vImsc)

{
    "batch_size": 64,
    "lr": 0.00005,
    "dropout": 0.5,
    "alpha": [
        0.4,
        0.6
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.830164	0.902029	0.661092	0.825981	0.813953	0.810022
Q	SE	SP	PPV	NPV
0.830164	0.806128	0.8542	0.846837	0.81502

6mA_T.thermophile (rVXQG)

{
    "batch_size": 32,
    "lr": 0.00005,
    "dropout": 0.1,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.87473	0.94		0.89
Q	SE	SP	PPV	NPV
	0.95	0.81

Mention: some problems lead to the interrupt during the training process, this is the result before interrupt.

6mA_Xoc BLS256 (owHB7)

{
    "batch_size": 64,
    "lr": 0.0001,
    "dropout": 0.7,
    "alpha": [
        0.5,
        0.5
    ]
}

ACC	AUC	MCC	F1	F2	F3
0.882131	0.951329	0.764302	0.881518	0.878778	0.877868
Q	SE	SP	PPV	NPV
0.882131	0.876961	0.887301	0.886123	0.87822