RAUR is to re-align the reads that can not be mapped by alignment tools. It takes advantages of the base quality scores (reported by the sequencer) to figure out the longest segment of a read with at
EPGA is used for paired-read libraries of read length shorter than 40bp and coverage larger than 100.
EPGA2 updates some modules in EPGA which can improve memory efficiency in genome asssembly. The read library for EPGA2 should be paired-end reads. Read length shorter than 50bp and coverage larger th
ISEA is an de novo assembly tool based using paired-end information and insert size distribution.
finds clusters (highly interconnected regions, protein complexes or functional module) in a network using various clustering algorithms.
is a Cytoscape app for analysis and visualization of clusters from network, and has been tested on Cytoscape 3.0.X. Three different graph clustering algorithms (HC-PIN, OH-PIN, IPCA) were implemented
is a scalable platform, in which a series of evaluation methods, such as recall, precision, sensitivity, specificity, p-value, and function enrichment, are implemented. Moreover, nine clustering algo
Modifying the DPClus algorithm for identifying protein complexes based on new topological structures.
HC-PIN is a fast hierarchical clustering algorithm based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network.
Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data,Include two jar files: TSN-PCD.jar and DFM-CIN.jar. TSN-PCD.jar .
is a Cytoscape app for network centrality analysis. This app supports eight typical centralities: Betweeness Centrality, Closeness Centrality, Degree Centrality, Eigenvector Centrality, Local Average
CPPK predicts new essential proteins based on network topology. CEPPK predicts new essential proteins by integrating network topology and gene expressions.
LAC is a local centrality based on the integration of protein-protein interaction and gene expression data.
Pec is a new centrality measure based on the integration of protein-protein interaction and gene expression data.
SON is a novel essential protein prediction method which integrates the information of subcellular localization, orthologous proteins and protein-protein interaction networks.
PUDT is a tool for identifying drug-target interaction based on PU learning. It divided unlabeled data U into reliable negative set RN and likely negative set LN, based on different target informatio
LDAP (lncRNA-disease association predictor), by integrating multiple biological data resources.Long noncoding RNAs are a biggest class of non-coding RNA with greater than 200nt in length which can re
Detect SNP-SNP interactions for case-control studies.
Detecting genome-wide epistases based on the clustering of relatively frequent items,it is a simple, fast and effective
algorithm to detect genome-wide multi-locus epistatic interactions
based on t
Detecting multi-SNP combinations for case-control studies.
MBiRW is one novel computational method, which utilizes comprehensive similarity measures and Bi-Random walk algorithm to identify potential novel indications for a given drug.
DRRS, a novel computational drug repositioning method using low-rank matrix appropriation and randomized algorithm, is used for predicting drug indications by integrating related data sources and val
BNNR is a novel computational method, which utilizes Bounded Nuclear Norm Regularization algorithm to identify potential novel indications for known or new drugs. The code in this package implements
CpGpeak, a peak detection method using the characteristics of CpG islands. Unlike the traditional peak detection methods that rely entirely on experimental data statistics and simulation results, CpG
DeepSignal constructs a BiLSTM+Inception structure to detect DNA methylation state from Nanopore reads. It is built with Tensorflow 1.8 and Python 3
A miRNA target prediction method based on matrix completion algorithm.
(virus-receptor interaction predictor) is used to
predict virus-receptor interactions of human. IILLS integrates the known
virus-receptor interactions and Amino acid sequences of receptor to