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A model to predict the function of hypothetical proteins through a nine-point classification scoring schema

Publication Type : Journal Article

Publisher : BMC Bioinformatics

Source : BMC Bioinformatics. 2019 Jan 8;20(1):14

Url : https://pubmed.ncbi.nlm.nih.gov/30621574/

Keywords : Classification Features, Functional Genomics, Hypothetical Proteins, Machine Learning

Campus : Amritapuri

School : School of Biotechnology

Department : biotechnology

Year : 2019

Abstract : Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.

Cite this Research Publication : Ijaq J, Malik G, Kumar A, Das PS, Meena N, Bethi N, Sundararajan VS, Suravajhala P. A model to predict the function of hypothetical proteins through a nine-point classification scoring schema. BMC Bioinformatics. 2019 Jan 8;20(1):14. doi: 10.1186/s12859-018-2554-y. PMID: 30621574; PMCID: PMC6325861.

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