• DyNetViewer
	DyNetViewer is a Cytoscape application that provides a range of functionalities for the construction,analysis and visualization of dynamic protein-protein interaction networks. [Link] [Paper]

• CytoNCA
	CytoNCA supports eight different centrality measures and each can be applied to both weighted and unweighted biological networks.It allows users to upload biological information of both nodes and edges in the network, to integrate biological data with topological data to detect specific nodes. [Link] [Paper]

• Cytocluster
	CytoCluster is a cytoscape plugin integrating six clustering algorithms,HC-PIN,OH-PIN,PCA,ClusterONE,DCU,IPC-MCE,and BinGO function to detect protein complexes or functional modules. [Link] [Paper]

• ClusterViz
	ClusterViz is an APP of Cytoscape 3 for cluster analysis and visualization in order to reduce complexity and enable extendibility for ClusterViz.It also fascinates the comparison of the results of different algorithms to do further related analysis. [Link] [Paper]

• CytoCtrlAnalyser
	CytoCtrlAnalyser is a Cytoscape app to provide a comprehensive platform for analyzing controllability of biomolecular networks. Nine algorithms have been integrated in CytoCtrlAnalyser. [Link] [Paper]

• HC-PIN
	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. [Link] [Paper]

• DFM-CIN
	DFM-CIN is a new framework to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. [Link] [Paper]

• IPCA
IPCA is a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density based on the study of known complexes in protein networks. [Link] [Paper]

• LoopPredictor
	LoopPredictor: Predicting unknown enhancer-mediated genome topology by an ensemble machine learning model. [Link] [Paper]

• NID
	NID is a comprehensive user-friendly web server for the prioritization and analysis of disease-related (candidate) genes, by which the users can easily get the results of disease-related genes by three search patterns (genomic location, cytogenetic location and whole network genome), subnetwork visualization, enrichment analysis and external links of the genes. [Link] [Paper]

• NIDM
NIDM is a framework of network impulsive dynamics on multiplex biological network to predict disease-related genes, which identifies disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. [Link] [Paper]

• BACPI
	BACPI is a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. [Link] [Paper]

• PROBselect
	PROBselect suggests a predictor that is likely to provide the best prediction of protein-binding residues the for the input proteins. PROBselect uses predictions generated by SCRIBER and estimated AUC of SSWRF and CRFPPI to make the recommendation. [Link] [Paper]

• DeepDISOBind
	DeepDISOBind provides predictions of the disordered residues that interact with proteins, DNA and RNA. [Link] [Paper]

• DeepDTAF
DeepDTAF: a deep learning method to predict protein–ligand binding affinity.. [Link] [Paper]

• DeepPFP-CO
DeepPFP-CO uses Graph Convolutional Network to explore and capture the co-occurrence of GO terms to improve the prediction of protein function. [Link]

• SSRE
	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement [Link] [Paper]

• NIMCE
	NIMCE: A Gene Regulatory Network Inference Approach Based on Multi Time Delays Causal Entropy [Link] [Paper]

• SinNLRR
	SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation [Link] [Paper]

• BiXGBoost
	BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks. [Link] [Paper]

• DeepLncLoc
	DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding. [Link] [Paper]

• EP-GBDT
	EP-GBDT: improving human essential protein prediction using only protein sequences via ensemble learning. [Link] [Paper]

• SCOP
SCOP: a novel scaffolding algorithm based on contig classification and optimization. [Link] [Paper]

• MAC
MAC: Merging Assemblies by Using Adjacency Algebraic Model and Classification. [Link] [Paper]

• iLSLS
iLSLS: A Novel Scaffolding Algorithm Based on Contig Error Correction and Path Extension. [Link] [Paper]

• HyMM
HyMM is a hybrid method integrating multiscale module structure, which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. [Link] [Paper]

• PrGeFNE
PrGeFNE is a novel method for predicting disease-related genes by using fast network embedding, which can integrate multiple types of associations related to diseases and genes. [Link] [Paper]

• DPCMNE
Detecting protein complexes from protein-protein interaction networks via multi-level network embedding. [Link] [Paper]

• TemporalspatialHub
Temporal-spatial analysis of the essentiality of hub proteins in protein-protein interaction networks. [Link]

• NetEPD
NetEPD: an integrated information platform for networkbased essential protein discovery. [Link] [Paper]

• CPPK&CEPPK
	CPPK predicts new essential proteins based on network topology. CEPPK predicts new essential proteins by integrating network topology and gene expressions. [Link] [Paper]

• PeC
Pec is a new centrality measure based on the integration of protein-protein interaction and gene expression data.The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. [Link] [Paper]

• LAC
LAC is a local centrality based on the integration of protein-protein interaction and gene expression data.The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. [Link] [Paper]

• MBiRW

MBiRW is a novel computational method named to utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. [Link] [Paper]

• DRRS

DRRS is a drug repositioning recommendation system, which can complete the association matrix of drug-disease heterogeneous network based on the Singular Value Thresholding (SVT) algorithm. A recycling rank revealing randomized singular value decomposition algorithm (R4SVD) is employed to fast and adaptively approximate the dominant singular values and their associated singular vectors so that the recommendation system is scalable to handle large adjacency matrices generated from large-scale drug-disease networks. [Link] [Paper]

• RWHNDR
	RWHNDR is a novel drug repositioning method by implementing random walk on the drug-target-disease network. By integrating multi-source data and exploiting global heterogeneous network information, it can predict and prioritize potential drugs for diseases effectively. [Paper]

• PUDT
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 information. [Link] [Paper]

• SIMCLDA

SIMCLDA is a method to predicte potential lncRNA-disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA-disease interactions and functional similarity of diseases based on disease-gene and gene-gene onotology associations. Then, we extract primary feature vectors from Gaussian interaction profile kernel of lncRNAs and functional similarity of diseases by principal component analysis, respectively. [Link] [Paper]

• ISEA
	ISEA is an iterative seed-extension algorithm for de novo assembly, ISEA utilizes reads overlap and paired-end information to correct error reads before assemblying, and uses an elaborately designed score function based on paired-end information and the distribution of insert size to solve the repeat region problem. [Link] [Paper]

• EPGA
	EPGA extracts paths from De Bruijn graph for genome assembly. EPGA uses a new score function to evaluate extension candidates based on the distributions of reads and insert size. The distribution of reads can solve problems caused by sequencing errors and short repetitive regions. [Link] [Paper]

• EPGA2
	EPGA2 is an updated version of EPGA, which applies some new modules and can bring about improved assembly results in small memory. EPGA2 adopts memory-efficient DSK to count K-mers and revised BCALM to construct De Bruijn Graph. Moreover, EPGA2 parallels the step of Contigs Merging and adds Errors Correction in its pipeline. [Link] [Paper]

• BOSS
	BOSS employs paired reads for scaffolding, and utilizes the distribution of insert size to decide whether an edge between two contigs should be added and how an edge should be weighed, then adopts an iterative strategy to detect spurious edges. [Link] [Paper]

• MEC
	MEC is a contig error correction method, which identifies candidate regions of misassemblies based on fragment coverage and read coverage, and then reduces the number of false misassemblies from candidates based on paired-end reads distribution and GC-content. [Link] [Paper]

• PECC
	PECC identifies and corrects misassembly errors in contigs based on the paired-end read distribution. PECC extracts sequence regions with lower paired-end reads supports and verifies them based on the distribution of paired-end supports. [Paper]

• GapReduce
	GapReduce is a gap filling method, which can fill the gaps using the paired reads, and GapReduce designs a novel approach that simultaneously considers k-mer frequency and distribution of paired reads based on the partitioned read sets. [Link]

• LDAP
	LDAP is a free web server for lncRNA-disease association prediction. It took the input lncRNA sequence in fasta format, either a pasted sequence (length > 200bp) or a file. Then the sequence similarity between input lncRNA and database is calculated by using Smith-Waterman algorithm. [Link] [Paper]

• CSA
	CSA is developed for the whole process of ChIP-Seq analysis, which covers mapping,quality control,peak calling and downstream analysis.In addition,CSA provides a customization function for users to define their own workflow.Moreover,the visualization of mapping,peak calling, motif finding,and pathway analysis results is also supplied in CSA. [Link]