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qust-aibbdrc's Projects

ppisp-xgboost icon ppisp-xgboost

Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis

rbpro-rf icon rbpro-rf

RBPro-RF: predicting RNA-binding proteins via random forest with elastic net

rl-gensvm icon rl-gensvm

Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine

rpi-mdlstack icon rpi-mdlstack

RPI-MDLStack: predicting RNA-protein interactions through deep learning with stacking strategy and LASSO

scgmai icon scgmai

scGMAI: a Gaussian mixture model for clustering single-cell RNA-seq data based on deep autoencoder

scigans icon scigans

Generative adversarial networks for single-cell RNA-seq imputation

scirpy icon scirpy

A scanpy extension to analyse single-cell TCR and BCR data.

sgl-svm icon sgl-svm

SGL-SVM: a novel method for tumor classification via support vector machine with sparse group Lasso

simlr_py icon simlr_py

Python Implementation of SIMLR for single-cell visualization and analysis

stackpdb icon stackpdb

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

stackppi icon stackppi

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

stackram icon stackram

StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble

submito-xgboost icon submito-xgboost

SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

sulsite-gtb icon sulsite-gtb

SulSite-GTB：identification of protein S-sulfenylation sites by fusing multiple feature information and Gradient tree Boosting

ubisite-xgboost icon ubisite-xgboost

Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier

ubisitepred icon ubisitepred

UbiSitePred: a novel method for improving accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components

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