Krampis, K.; Ross, E.; Ogunwobi, O.; Ma, G.; Mazumder, R.; Wultsch, C. Principles of Artificial Neural Networks and Machine Learning for Bioinformatics Applications. Preprints2023, 2023060042. https://doi.org/10.20944/preprints202306.0042.v1
APA Style
Krampis, K., Ross, E., Ogunwobi, O., Ma, G., Mazumder, R., & Wultsch, C. (2023). Principles of Artificial Neural Networks and Machine Learning for Bioinformatics Applications. Preprints. https://doi.org/10.20944/preprints202306.0042.v1
Chicago/Turabian Style
Krampis, K., Raja Mazumder and Claudia Wultsch. 2023 "Principles of Artificial Neural Networks and Machine Learning for Bioinformatics Applications" Preprints. https://doi.org/10.20944/preprints202306.0042.v1
Abstract
With the exponential growth of machine learning and development of Artificial Neural Network (ANNs) in recent years, there is great opportunity to leverage this approach and accelarate bio-logical discoveries through applications on the analysis of bioinformatics data. Various types of datasets including for example protein or gene interaction networks, molecular structures and cellular signalling pathways, have already been used for machine learning by training ANNs for inference and pattern classification. However, unlike regular data structures that are commonly used in the computer science and engineering fields, bioinformatics datasets present challenges that require unique algorithmic approaches. The recent development of the geometric and deep learning approach within the machine learning field, is very promising towards accelerating analysis complex bioinformatics datasets. The principles of ANNs and their importance for bio-informatics machine learning is demonstrated herein, through presentation of the undelying mathematical and statistical foundations from group theory, symmetry, linear algebra. Further-more, the structure and functions of ANN algorithms that form the core principles of artificial intelligence are explained, in relation to the bioinformatics data domain. Overall, the manuscript provides guidance for researchers to understand the principles required for practicing machine learning and artificial intelligence, with the special considerations towards bioinformatics applications.
Keywords
machine learning; artificial intelligence; bioinformatics; cancer biology; neural networks; sym-metry; group theory; algorithms
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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