https://github.com/aspirincode/lgbm-dtis

LGBM-DTIs: Predicting drug-target interactions using LightGBM with protein multi-information and molecular structure
https://github.com/aspirincode/lgbm-dtis

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LGBM-DTIs: Predicting drug-target interactions using LightGBM with protein multi-information and molecular structure

• Host: GitHub
• URL: https://github.com/aspirincode/lgbm-dtis
• Owner: AspirinCode
• Created: 2020-12-20T09:17:58.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-20T05:09:52.000Z (over 5 years ago)
• Last Synced: 2025-01-29T07:30:30.958Z (about 1 year ago)
• Size: 1.04 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
##LGBM-DTIs

LGBM-DTIs：Predicting drug-target interactions using LightGBM with protein multi-information and molecular structure.

###LGBM-DTIs uses the following dependencies:

* MATLAB2014a

* python 3.6 

* numpy

* pandas

* scikit-learn

* imblearn 

###Guiding principles:

**The dataset file contains the gold standard dataset, Kuang dataset and network dataset.

**feature extraction:

1) Evolutionary-based features: PsePSSM.m is the implementation of PsePSSM. 

2) Sequence-based features: PAAC.py is the implementation of PseAAC.

3) Structural-based features: Structure.py is the implementation of structural information based on SPIDER3.

   

** feature selection:

   Lasso.py represents the Lasso.

   Elastic_net.py represents the elastic net.

   ET.py represents the extra trees.

   IG.py represents the information gain.

   LLE.py represents the locally linear embedding.

   MI.py represents the mutual information.

   

** data preprocessing:

   SMOTE.R is the implementation of SMOTE. 

   AllKNN.py is the implementation of AllKNN. 

   ENN.py is the implementation of edited nearest neighbours. 

   OSS.py is the implementation of one-sided selection. 

   RUS.py is the implementation of random undersampling.

** Classifier:

   LightGBM.py is the implementation of LightGBM.

   AdaBoost.py is the implementation of AdaBoost.

   DT.py is the implementation of decision tree. 

   GBM.py is the implementation of gradient boosting machine.

   KNN.py is the implementation of K-nearest neighbor. 

   LR.py is the implementation of logistic regression. 

   NB.py is the implementation of NB.

   RF.py is the implementation of random forest. 

   SVM.py is the implementation of support vector machine.