ArticleBriefings in bioinformatics2017
A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 52 citations in OpenAlex.
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- Protein-Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow.Life (Basel, Switzerland) · 2022Review
- Virus-host interaction analysis in colorectal cancer identifies core virus network signature and small molecules.Computational and structural biotechnology journal · 2022Article
- Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.Computational and mathematical methods in medicine · 2022Article
- A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.American journal of human genetics · 2021Article
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- Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.International journal of molecular sciences · 2020Article
- kGCN: a graph-based deep learning framework for chemical structures.Journal of cheminformatics · 2020Article
- A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches.Scientific reports · 2020Article
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- Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.Frontiers in pharmacology · 2020Article
- Human SNORA31 variations impair cortical neuron-intrinsic immunity to HSV-1 and underlie herpes simplex encephalitis.Nature medicine · 2019Article
- Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.Journal of chemical information and modeling · 2019Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.