Identification of Heat-Resistant Bacteria Based on Selection of Proper Representation of Protein Sequences Using Deep Learning Approach | Zendy

Reza Ahsan | Zendy; Mansour Ebrahimi | Zendy

Open Access

Identification of Heat-Resistant Bacteria Based on Selection of Proper Representation of Protein Sequences Using Deep Learning Approach

Author(s) -

Reza Ahsan,

Mansour Ebrahimi

Publication year - 2020

Publication title -

qom univ med sci j

Language(s) - English

Resource type - Journals

eISSN - 2008-1375

pISSN - 1735-7799

DOI - 10.29252/qums.14.3.54

Subject(s) - identification (biology) , selection (genetic algorithm) , representation (politics) , artificial intelligence , bacteria , deep learning , computational biology , computer science , machine learning , pattern recognition (psychology) , biology , genetics , ecology , politics , political science , law

Received: 2 Jan, 2020 Accepted: 7 Jun, 2020 Abstract Background and Objectives: Identification of effective mechanisms in heat-resistance in bacteria is of great importance in some industries, such as food industry, textile manufacturing, and especially in detergent production industries. For this purpose, deep learning tools were used to identify the characteristics of heat-resistant bacteria based on protein properties.

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