Machine Learning Algorithms in Natural Science

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1455

Special Issue Editors


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Department of Mathematics, Computer Science and Physics, Via delle Scienze 206, 33100 Udine, Italy
Interests: algorithms for bioinformatics and computational biology; logic decision algorithms; algorithms and data-structures for compressed computation
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Special Issue Information

Dear Colleagues,

Machine learning algorithms are very powerful tools for analyzing data and learning patterns which they use to make predictions. They have been applied in many fields and areas of knowledge with great success. In the field of natural sciences there are innumerable problems resulting from large amounts of data being generated and it being necessary to carry out analysis of the information to solve different problems. Thus, machine learning could be applied to verify hypotheses about the existence of common characteristics between species; search for patterns in seismic phenomena and other natural phenomena; and search for patterns of behavior in animals, classification of animals, plants, living beings, or the environment. That is why the natural sciences constitute an ideal field of application for this type of algorithms and in particular for artificial intelligence techniques. In this Special Issue, we are open to any proposal that focuses on solving a problem in the field of natural sciences via the use of machine learning techniques. In particular, the following topics will be of interest to us:

  • Application of ML techniques to natural phenomena (meteorology, seismology, volcanism, etc.).
  • Application of ML techniques to classification problems in the field of natural sciences (animals, plants, viruses, diseases, etc.).
  • Genetic analysis. Genomics.
  • Problems of prediction and disease analysis.
  • Epidemic prediction problems.
  • Problems of prediction and analysis in the field of geology.
  • Problems of prediction and analysis in the field of zoology.

Dr. Antonio Sarasa-Cabezuelo
Prof. Dr. Alberto Policriti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • application of ML techniques to natural phenomena (meteorology, seismology, volcanism, etc.)
  • application of ML techniques to classification problems in the field of natural sciences (animals, plants, viruses, diseases, etc.)
  • genetic analysis. genomics
  • problems of prediction and disease analysis
  • epidemic prediction problems
  • problems of prediction and analysis in the field of geology
  • problems of prediction and analysis in the field of zoology

Published Papers (1 paper)

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Research

14 pages, 2002 KiB  
Article
A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
by Leyla Nazari, Vida Ghotbi, Mohammad Nadimi and Jitendra Paliwal
Algorithms 2023, 16(9), 407; https://doi.org/10.3390/a16090407 - 27 Aug 2023
Cited by 2 | Viewed by 1077
Abstract
This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes [...] Read more.
This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes was analyzed using several machine learning tools including the synthetic minority oversampling technique (SMOTE), information gain (IG), ReliefF, and least absolute shrinkage and selection operator (LASSO), along with various classifiers (BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest). We identified 439 differentially expressed genes (DEGs), of which only three were down-regulated (AT3G20810, AT1G31680, and AT1G30250). The performance of the top 20 genes selected by IG and ReliefF was evaluated using the classifiers mentioned above to classify stressed versus non-stressed samples. The random forest algorithm outperformed other algorithms with an accuracy of 97.91% and 98.51% for IG and ReliefF, respectively. Additionally, 42 genes were identified from all 30,380 genes using LASSO regression. The top 20 genes for each feature selection were analyzed to determine three common genes (AT5G44050, AT2G47180, and AT1G70700), which formed a three-gene signature. The efficiency of these three genes was evaluated using random forest and XGBoost algorithms. Further validation was performed using an independent RNA_seq dataset and random forest. These gene signatures can be exploited in plant breeding to improve stress tolerance in a variety of crops. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Natural Science)
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