Abstract:

April 12, 2019 ยท View on GitHub

DeepCNF_Package (v1.02) date: 2015.05.30

Title: Training Conditional Random Fields by Maximizing AUC

Author: Sheng Wang Contact email: realbigws@gmail.com

References:

  1. AUC-maximized Deep Convolutional Neural Fields for Protein Sequence Labeling Sheng Wang#, Siqi Sun, Jinbo Xu ECML/PKDD, 2016 https://link.springer.com/chapter/10.1007/978-3-319-46227-1_1

  2. AUCpreD: Proteome-level Protein Disorder Prediction by AUC-maximized Deep Convolutional Neural Fields Sheng Wang#, Jianzhu Ma, Jinbo Xu ECCB, 2016 Bioinformatics, 2016 https://academic.oup.com/bioinformatics/article-abstract/32/17/i672/2450776

========= Abstract:

  1. The training program of Deep Convolutional Neural Filed ( DeepCNF ) for maximizing (1) log probability, (2) posterior probability , or (3) AUC value.

  2. The predicting program of DeepCNF for a given feature file by using a trained model.

======== Install:

  1. download the package

git clone https://github.com/realbigws/DeepCNF_AUC/ cd DeepCNF_AUC/

+++++++++++++++++++++

  1. compile

2.1 compile under single CPU

cd source_code/ make cd ../

2.2 compile under MPI/OpenMP

cd source_code/ make mpi cd ../

====== Usage:

  1. DeepCNF_Train

Usage :

mpirun -np NP ./DeepCNF_Train -r train_file -t test_file -w window_str -d node_str -s state_num -l feat_num [-S feat_select] [-n finetune_num] [-f finetune_reg] [-m init_model] [-o out_root] [-G gate_function] [-W label_weight] [-M method] [-D AUC_degree] Options:

-np NP : number of processors.

-r train_file : training file.

-t test_file : testing file.

-w window_str : window string for DeepCNF. e.g., '5,5'

-d node_str : node string for DeepCNF. e.g., '40,20'

-s state_num : state number.

-l feat_num : feature number at each position.

-S feat_select : feature selection. e.g., '1-7,9' [default uses all features]

-n finetune_num : fine-tune iteration number. [default is 200]

-f finetune_reg : fine-tune regularizer. [default is 0.5]

-m init_model : file for initial model parameters. [optional, and default is NULL]

-o out_root : output directory for trained models. [optional, and default is 'MODELS/']

-G gate_function : gate function type: sigmoid (1), tanh (2), and relu (0). [default is 1]

-M method : maximize log_prob (0), posterior_prob (1), or AUC (2). [default is 0]

-W label_weight : label weight. e.g., '0.1,0.9'. [default is 1 for each label]

-D AUC_degree : degree (1-30) of polynomials for AUC [default is 3].

  1. DeepCNF_Pred

Usage :

./DeepCNF_Pred -i input_file -w window_str -d node_str -s state_num -l feat_num -m init_model [-S feat_select] Options:

-i input_file : input feature file.

-w window_str : window string for DeepCNF. e.g., '5,5'

-d node_str : node string for DeepCNF. e.g., '40,20'

-s state_num : state number.

-l feat_num : feature number at each position.

-m init_model : file for trained model.

-S feat_select : feature selection. e.g., '1-7,9' [default uses all features]

======== Example:

  1. Maximize log probability:

cd examples/ ./DeepCNF_Train -r 2fzlA.reso -t 2fzlA.reso -w 5 -d 5 -s 3 -l 40 -n 50 cd ../

  1. Maximize AUC value:

cd examples/ ./DeepCNF_Train -r 1kq1S.feat -t 1kq1S.feat -w 5 -d 5 -s 2 -l 129 -n 50 -M 2 cd ../

  1. Predict the labels of a given feature file by using a certain model:

cd examples/ ./DeepCNF_Pred -i 1kq1S.feat -w 5 -d 5 -s 2 -l 129 -m model.10 cd ../

  1. make feature file for DeepCNF

cd examples/ ./DeepCNF_FeatMake 5fgnA.profile 5fgnA.label > 5fgnA.feat cd ../

================== Input file format:

  1. Training data format

//-> data format ( length, features, labels [should be authentic] ) //-> example below:

78 feat1 feat2 feat3, ..., featN .... (with 78 feature lines) 0 1 0 2 ... ( with 78 label lines) 54 feat1 feat2 feat3, ..., featN .... (with 54 feature lines) 0 1 0 2 ... ( with 54 label lines)

  1. Testing data format

//-> data format ( length, features, labels [can be anything] ) //-> example below:

78 feat1 feat2 feat3, ..., featN .... (with 78 feature lines) -1 -1 -1 -1 ... ( with 78 label lines) 54 feat1 feat2 feat3, ..., featN .... (with 54 feature lines) -1 -1 -1 -1 ... ( with 54 label lines)