• BiXGBoost
MSTD
BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks. [Link] [Paper]
• DyNetViewer
MSTD
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]
• CytoCtrlAnalyser
MSTD
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]
• Cytocluster
MSTD
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
MSTD
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]
• HC-PIN
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
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]
• CytoNCA
MSTD
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]
• CPPK&CEPPK
MSTD
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]
• PROBselect
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
DeepDISOBind provides predictions of the disordered residues that interact with proteins, DNA and RNA. [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]

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