Apr 20, 2020

ProtTox: Toxin identification from Protein Sequences

BioRxiv : the Preprint Server for Biology
Debanjan DattaN. Ramakrishnan

Abstract

Toxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method , that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

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Mentioned in this Paper

Molecular Dynamics
In Vivo
Theoretical Model
Classification
Energy Transfer
Genome
Interphase
Interphase Chromosome
Chromosomes
Structure

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