Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 22; no. 1; pp. 239 - 17
Main Authors:	Boone, Kyle, Wisdom, Cate, Camarda, Kyle, Spencer, Paulette, Tamerler, Candan
Format:	Journal Article
Language:	English
Published:	London BioMed Central 11.05.2021 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Amino Acid Sequence Antibacterial Antibacterial agents Antibiotic resistance Antibiotics Antiinfectives and antibacterials Antimicrobial activity Antimicrobial agents Antimicrobial Cationic Peptides Antimicrobial peptide Antimicrobial peptides Bacteria Bioinformatics Biomedical and Life Sciences Biomimetics Chemical compounds Codon Combinatorial analysis Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Customization Decision making Deep learning Deoxyribonucleic acid Design DNA Drug development Drug resistance Drug Resistance, Microbial Genetic algorithm Genetic algorithms Identification and classification Immune system Learning algorithms Life Sciences Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Microarrays Microbiological research Microorganisms Neural networks Nucleotide sequence Peptides Pharmacology Pore Forming Cytotoxic Proteins Research Article Rough set theory Set theory United States United States > US Rough set theory Antibacterial Genetic algorithm Machine learning Antimicrobial peptide
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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