gcn for prediction of protein interactions，图卷积用于蛋白质相互作用。.zip

# gcn for prediction of protein interactions 利用各种图神经网络进行link prediction of protein interactions。 **Guide** - [Intro](#Intro) - [Model](#Model) - [Dataset](#Dataset) - [Reference](#reference) ## Intro 目前主要实现基于【data/yeast/yeast.edgelist】下的蛋白质数据进行link prediction。 ## Model ### 模型 模型主要使用图神经网络，如gae、vgae等 * 1.GCNModelVAE(src/vgae)：图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)， [Variational Graph Auto-Encoders](https://arxiv.org/pdf/1611.07308.pdf) 。  * 2.GCNModelARGA(src/arga)：对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器， [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://arxiv.org/pdf/1802.04407v2.pdf) 。  #### Usage - 相关参数的配置config见每个模型文件夹中的config.cfg文件，训练和预测时会加载此文件。 - 训练及预测 ##### 1.GCNModelVAE(src/vgae) (1).训练 ``` from src.vgae.train import Train train = Train() train.train_model('config.cfg') ``` ``` Epoch: 0001 train_loss = 0.73368 val_roc_score = 0.77485 average_precision_score = 0.69364 time= 0.79382 Epoch: 0002 train_loss = 0.73334 val_roc_score = 0.80637 average_precision_score = 0.74248 time= 0.78920 Epoch: 0003 train_loss = 0.73341 val_roc_score = 0.85901 average_precision_score = 0.84317 time= 0.78759 Epoch: 0004 train_loss = 0.73353 val_roc_score = 0.86936 average_precision_score = 0.85909 time= 0.78880 Epoch: 0005 train_loss = 0.73334 val_roc_score = 0.86945 average_precision_score = 0.86092 time= 0.78438 Epoch: 0006 train_loss = 0.73353 val_roc_score = 0.87117 average_precision_score = 0.86205 time= 0.78761 Epoch: 0007 train_loss = 0.73352 val_roc_score = 0.87235 average_precision_score = 0.86407 time= 0.78210 Epoch: 0008 train_loss = 0.73338 val_roc_score = 0.87317 average_precision_score = 0.86462 time= 0.78477 Epoch: 0009 train_loss = 0.73341 val_roc_score = 0.87462 average_precision_score = 0.86755 time= 0.78378 Epoch: 0010 train_loss = 0.73348 val_roc_score = 0.87606 average_precision_score = 0.86853 time= 0.78587 Epoch: 0011 train_loss = 0.73344 val_roc_score = 0.87686 average_precision_score = 0.86923 time= 0.78406 Epoch: 0012 train_loss = 0.73331 val_roc_score = 0.87665 average_precision_score = 0.86880 time= 0.78253 Epoch: 0013 train_loss = 0.73357 val_roc_score = 0.87426 average_precision_score = 0.86521 time= 0.78202 Epoch: 0014 train_loss = 0.73327 val_roc_score = 0.87218 average_precision_score = 0.86192 time= 0.78299 Epoch: 0015 train_loss = 0.73336 val_roc_score = 0.87118 average_precision_score = 0.85946 time= 0.78166 Epoch: 0016 train_loss = 0.73336 val_roc_score = 0.86960 average_precision_score = 0.85835 time= 0.78792 Epoch: 0017 train_loss = 0.73355 val_roc_score = 0.87126 average_precision_score = 0.85940 time= 0.78401 Epoch: 0018 train_loss = 0.73357 val_roc_score = 0.87050 average_precision_score = 0.85648 time= 0.78511 Epoch: 0019 train_loss = 0.73332 val_roc_score = 0.86737 average_precision_score = 0.84906 time= 0.78132 Epoch: 0020 train_loss = 0.73345 val_roc_score = 0.86632 average_precision_score = 0.84532 time= 0.78603 test roc score: 0.863696753293295 test ap score: 0.8381410617542567 ``` (2).预测 ``` from src.vgae.predict import Predict predict = Predict() predict.load_model_adj('config_cfg') # 会返回原始的图邻接矩阵和经过模型编码后的hidden embedding经过内积解码的邻接矩阵，可以对这两个矩阵进行比对，得出link prediction. adj_orig, adj_rec = predict.predict() ``` ##### 2.GCNModelARGA(src/arga) (1).训练 ``` from src.arga.train import Train train = Train() train.train_model('config.cfg') ``` ``` Epoch: 0001 train_loss = 2.17176 val_roc_score = 0.77090 average_precision_score = 0.69050 time= 0.81113 Epoch: 0002 train_loss = 2.16173 val_roc_score = 0.84636 average_precision_score = 0.81340 time= 0.81458 Epoch: 0003 train_loss = 2.14979 val_roc_score = 0.87660 average_precision_score = 0.86472 time= 0.80898 Epoch: 0004 train_loss = 2.13698 val_roc_score = 0.87735 average_precision_score = 0.86534 time= 0.80995 Epoch: 0005 train_loss = 2.12339 val_roc_score = 0.87765 average_precision_score = 0.86592 time= 0.80865 Epoch: 0006 train_loss = 2.10753 val_roc_score = 0.87756 average_precision_score = 0.86571 time= 0.80748 Epoch: 0007 train_loss = 2.08996 val_roc_score = 0.87806 average_precision_score = 0.86621 time= 0.80738 Epoch: 0008 train_loss = 2.06920 val_roc_score = 0.87801 average_precision_score = 0.86623 time= 0.80744 Epoch: 0009 train_loss = 2.04701 val_roc_score = 0.87795 average_precision_score = 0.86618 time= 0.80932 Epoch: 0010 train_loss = 2.02241 val_roc_score = 0.87830 average_precision_score = 0.86643 time= 0.80722 Epoch: 0011 train_loss = 1.99754 val_roc_score = 0.87807 average_precision_score = 0.86620 time= 0.80533 Epoch: 0012 train_loss = 1.97255 val_roc_score = 0.87749 average_precision_score = 0.86586 time= 0.80859 Epoch: 0013 train_loss = 1.94664 val_roc_score = 0.87607 average_precision_score = 0.86483 time= 0.80660 Epoch: 0014 train_loss = 1.92208 val_roc_score = 0.87408 average_precision_score = 0.86320 time= 0.80300 Epoch: 0015 train_loss = 1.89869 val_roc_score = 0.87290 average_precision_score = 0.86218 time= 0.80400 Epoch: 0016 train_loss = 1.87584 val_roc_score = 0.87244 average_precision_score = 0.86186 time= 0.80392 Epoch: 0017 train_loss = 1.85415 val_roc_score = 0.87554 average_precision_score = 0.86400 time= 0.80675 Epoch: 0018 train_loss = 1.83373 val_roc_score = 0.87653 average_precision_score = 0.86473 time= 0.80762 Epoch: 0019 train_loss = 1.81515 val_roc_score = 0.87718 average_precision_score = 0.86532 time= 0.80596 Epoch: 0020 train_loss = 1.79975 val_roc_score = 0.87745 average_precision_score = 0.86551 time= 0.80889 test roc score: 0.8797451083479302 test ap score: 0.8681038618348471 ``` (2).预测 ``` from src.arga.predict import Predict predict = Predict() predict.load_model_adj('config_cfg') # 会返回原始的图邻接矩阵和经过模型编码后的hidden embedding经过内积解码的邻接矩阵，可以对这两个矩阵进行比对，得出link prediction. adj_orig, adj_rec = predict.predict() ``` ## Dataset 数据来自酵母蛋白质相互作用[yeast](http://snap.stanford.edu/deepnetbio-ismb/ipynb/yeast.edgelist) 。 数据集的格式如下，具体可见[data](data/yeast/yeast.edgelist)。 ``` YLR418C YOL145C YOL145C YLR418C YLR418C YOR123C YOR123C YLR418C ...... ...... ``` ## Reference * [Variational Graph Auto-Encoders](https://arxiv.org/pdf/1611.07308.pdf) * https://github.com/zfjsail/gae-pytorch/blob/master/gae/utils.py * https://github.com/tkipf/gae/tree/master/gae * http://snap.stanford.edu/deepnetbio-ismb/ipynb/Graph+Convolutional+Prediction+of+Protein+Interactions+in+Yeast.html * [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://arxiv.org/pdf/1802.04407v2.pdf) * https://github.com/pyg-team/pytorch_geometric