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Algorithms
Special Issues
Machine Learning in Bioinformatics
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Machine Learning in Bioinformatics

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 (28 February 2022) | Viewed by 2330

Special Issue Editor

Dr. Shouyong Jiang

E-Mail Website
Guest Editor
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: optimisation methods; systems biology, industrial biotechnology; smart energy; intelligent greenhouses; maching learning; data science; computational intelligence; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an interdisciplinary field of science and technology, bioinformatics provides an important way to improve our understanding of biological data. Artificial intelligence has been employed in useful ways for many bioinformatics applications, ranging from DNA sequencing to protein classification, and to drug design, leading to numerous new discoveries. With the ever-increasing volumes and complexity of biological data, it is expected that the future of bioinformatics will be more closely associated with and dependent on artificial intelligence and machine learning. The aim of this Special Issue is to collect state-of-the-art research on the topic of bioinformatics using artificial intelligence. The Special Issue welcomes any relevant topics, methods, and applications that use artificial intelligence, machine learning or other technologies of the kind for bioinformatics, including (but not limited to) systems biology, synthetic biology, drug design, genomics, proteomics, microarrays analysis. 

Dr. Shouyong Jiang
Guest Editor

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

  • bioinformatics
  • machine learning
  • artificial intelligence
  • big data

Published Papers (1 paper)

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Research

19 pages, 5163 KiB  
Article
XGB4mcPred: Identification of DNA N4-Methylcytosine Sites in Multiple Species Based on an eXtreme Gradient Boosting Algorithm and DNA Sequence Information
by Xiao Wang, Xi Lin, Rong Wang, Kai-Qi Fan, Li-Jun Han and Zhao-Yuan Ding
Algorithms 2021, 14(10), 283; https://0-doi-org.brum.beds.ac.uk/10.3390/a14100283 - 29 Sep 2021
Cited by 1 | Viewed by 1675
Abstract
DNA N4-methylcytosine(4mC) plays an important role in numerous biological functions and is a mechanism of particular epigenetic importance. Therefore, accurate identification of the 4mC sites in DNA sequences is necessary to understand the functional mechanism. Although some effective calculation tools have been proposed [...] Read more.
DNA N4-methylcytosine(4mC) plays an important role in numerous biological functions and is a mechanism of particular epigenetic importance. Therefore, accurate identification of the 4mC sites in DNA sequences is necessary to understand the functional mechanism. Although some effective calculation tools have been proposed to identifying DNA 4mC sites, it is still challenging to improve identification accuracy and generalization ability. Therefore, there is a great need to build a computational tool to accurately identify the position of DNA 4mC sites. Hence, this study proposed a novel predictor XGB4mcPred, a predictor for the identification of 4mC sites trained using an extreme gradient boosting algorithm (XGBoost) and DNA sequence information. Firstly, we used the One-Hot encoding on adjacent and spaced nucleotides, dinucleotides, and trinucleotides of the original 4mC site sequences as feature vectors. Then, the importance values of the feature vectors pre-trained by the XGBoost algorithm were used as a threshold to filter redundant features, resulting in a significant improvement in the identification accuracy of the constructed XGB4mcPred predictor to identify 4mC sites. The analysis shows that there is a clear preference for nucleotide sequences between 4mC sites and non-4mC site sequences in six datasets from multiple species, and the optimized features can better distinguish 4mC sites from non-4mC sites. The experimental results of cross-validation and independent tests from six different species show that our proposed predictor XGB4mcPred significantly outperformed other state-of-the-art predictors and was improved to varying degrees compared with other state-of-the-art predictors. Additionally, the user-friendly webserver we used to developed the XGB4mcPred predictor was made freely accessible. Full article
(This article belongs to the Special Issue Machine Learning in Bioinformatics)
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