WeiBI (web-based platform): Enriching integrated interaction network with increased coverage and functional proteins from genome-wide experimental OMICS data

Author:

Kaushik Aman Chandra,Mehmood Aamir,Dai XiaofengORCID,Wei Dong-Qing

Abstract

AbstractMany molecular system biology approaches recognize various interactions and functional associations of proteins that occur in cellular processing. Further understanding of the characterization technique reveals noteworthy information. These types of known and predicted interactions, gained through multiple resources, are thought to be important for experimental data to satisfy comprehensive and quality needs. The current work proposes the “WeiBI (WeiBiologicalInteractions)” database that clarifies direct and indirect partnerships associated with biological interactions. This database contains information concerning protein’s functional partnerships and interactions along with their integration into a statistical model that can be computationally predicted for humans. This novel approach in WeiBI version 1.0 collects information using an improved algorithm by transferring interactions between more than 115570 entries, allowing statistical analysis with the automated background for the given inputs for functional enrichment. This approach also allows the input of an entity’s list from a database along with the visualization of subsets as an interaction network and successful performance of the enrichment analysis for a gene set. This wisely improved algorithm is user-friendly, and its accessibility and higher accuracy make it the best database for exploring interactions among genomes’ network and reflects the importance of this study. The proposed server “WeiBI” is accessible at http://weislab.com/WeiDOCK/?page=PKPD.

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Shanghai Jiao Tong University

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep centroid: a general deep cascade classifier for biomedical omics data classification;Bioinformatics;2024-02-01

2. Systems Approaches in Identifying Disease-Related Genes and Drug Targets;Systems Biology Approaches: Prevention, Diagnosis, and Understanding Mechanisms of Complex Diseases;2024

3. Advances and Trends in Omics Technology Development;Frontiers in Medicine;2022-07-01

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