NWPU-903PR

Pinned Repositories

DMDeepm6A1.0
A R package used to identify single base resolution m6A and differential m6A methylation site from MeRIP-seq data version 1.0.
Language:R4 1 41
DPDDI
DPDDI:a Deep Predictor for Drug-Drug Interactions
Language:Python8 2 14
Funm6AViewer
Identification and visualization of functional differential m6A methylation genes (FDmMGenes) and single base DmM sites.
Language:R4 2 11
HyperSynergy
Source code and dataset for TPAMI 2023 paper: "Few-Shot Drug Synergy Prediction with a Prior-Guided Hypernetwork Architecture"
Language:Python4 1 23
LPI-CNNCP
Language:Python40
LPI_BLS
Language:Python8 2 13
MTDDI
Language:Python4 2 12
ncRPI-LGAT
Language:Python42
NPBSS_MATLAB
NPBSS: A new PacBio sequencing simulator for generating the continuous long reads with an empirical model
Language:Matlab40
PNC
The PNC package is to identify personalized driver genes of an individual patient by using network control principle .
Language:MATLAB9 1 02

NWPU-903PR's Repositories

NWPU-903PR/DMDeepm6A1.0
A R package used to identify single base resolution m6A and differential m6A methylation site from MeRIP-seq data version 1.0.
Language:R4 1 41
NWPU-903PR/Funm6AViewer
Identification and visualization of functional differential m6A methylation genes (FDmMGenes) and single base DmM sites.
Language:R4 2 11
NWPU-903PR/HyperSynergy
Source code and dataset for TPAMI 2023 paper: "Few-Shot Drug Synergy Prediction with a Prior-Guided Hypernetwork Architecture"
Language:Python4 1 23
NWPU-903PR/MTDDI
Language:Python4 2 12
NWPU-903PR/ncRPI-LGAT
Language:Python42
NWPU-903PR/GNN-DDI
Language:Python31
NWPU-903PR/HGDC
A novel heterophilic graph diffusion convolutional net-work for identifying cancer driver genes
Language:Python3 2 03
NWPU-903PR/SEPT
Prediction of enhancer-promoter interactions using the cross-cell information and domain adversarial neural network
Language:Python3 2 2
NWPU-903PR/DGMP
Language:Python2 2 02
NWPU-903PR/IMCDriver
Language:Python22
NWPU-903PR/m6Aexpress
Language:R2 3 26
NWPU-903PR/CPGD
Language:MATLAB1 1 01
NWPU-903PR/m6AcancerNet
m6AcancerNet is a novel network-based approach to identify m6A-mediated driver genes in cancer.
Language:R11
NWPU-903PR/MTGCL
Language:Python1
NWPU-903PR/SGRL-DDI
Language:Python1
NWPU-903PR/3DSC-TF
Language:Python
NWPU-903PR/bioRFR
Language:R2 01
NWPU-903PR/circBDCNN
circBDCNN: distinguishing circular RNAs from other long non-coding RNAs with Broad Dilation Convolutional Neural Network
Language:Python2 0
NWPU-903PR/EMOGI
An explainable multi-omics graph integration method based on graph convolutional networks to predict cancer genes.
NWPU-903PR/IRRG
Language:R
NWPU-903PR/m6AexpressBHM
m6Aexpress-BHM: Predicting m6A regulation of gene expression in multiple-groups context by a Bayesian Hierarchical Mixture model
Language:R
NWPU-903PR/m6AexpressReader
Language:R
NWPU-903PR/PCoDG
Language:Python
NWPU-903PR/PDGMN
Language:Python2 01
NWPU-903PR/PDGPCS
PDPCST is a personalized driver genes prediction method
Language:MATLAB2 01
NWPU-903PR/PhenoDriver-Paper
Language:R1
NWPU-903PR/PhenoDriverR
Language:R1
NWPU-903PR/ROICSE
2 0
NWPU-903PR/SQVE
Language:Python
NWPU-903PR/TFBS_MLCNN
Language:Python2 0