Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Machine Learning in Mathematical and Computational Biology
share announcement

Machine Learning in Mathematical and Computational Biology

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 5036

Special Issue Editor

Prof. Dr. Fuyi Li

E-Mail Website
Guest Editor
Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC 3010, Australia
Interests: machine learning; data Mining; bioinformatics; biomacromolecular covalent modifications; host-pathology interaction.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last 30 years, machine learning has developed into a multifield interdisciplinary subject, involving the probability theory, statistics, approximation theory, convex analysis, computational complexity theory, and other disciplines. Machine learning has become a vital tool for many projects in computational biology, bioinformatics, and health informatics. The ever-expanding scale and inherent complexity of biological data have prompted the increasing use of machine learning in biology to build informative and predictive models of underlying biological processes.

In this Special Issue, we envision the application of machine learning to various biological models to demonstrate its utility in addressing these growing computational challenges. We invite you to contribute to all aspects of the topic "Machine Learning in Mathematical and Computational Biology".

Articles with sound methodology and scientific practice are particularly welcome. Relevant topics include, but are not limited to, the following:

  • Machine learning;
  • Bioinformatics;
  • Data mining;
  • Computational biology;
  • Biochemical research methods;
  • Biochemistry and molecular biology;
  • Deep learning.

Prof. Dr. Fuyi Li
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

  • machine learning
  • bioinformatics
  • data mining
  • computational biology
  • biochemical research methods
  • biochemistry and molecular biology
  • deep learning

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 4672 KiB  
Article
Asian Affective and Emotional State (A2ES) Dataset of ECG and PPG for Affective Computing Research
by Nor Azlina Ab. Aziz, Tawsif K., Sharifah Noor Masidayu Sayed Ismail, Muhammad Anas Hasnul, Kamarulzaman Ab. Aziz, Siti Zainab Ibrahim, Azlan Abd. Aziz and J. Emerson Raja
Algorithms 2023, 16(3), 130; https://doi.org/10.3390/a16030130 - 27 Feb 2023
Cited by 3 | Viewed by 2602
Abstract
Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and [...] Read more.
Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and avoiding bias in this field. This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2ES) dataset, for affective computing research. The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various ethnicities. The subjects were exposed to 25 carefully selected audio–visual stimuli to elicit specific targeted emotions. An analysis of the participants’ self-assessment and a list of the 25 stimuli utilised are also presented in this work. Emotion recognition systems are built using ECG and PPG data; five machine learning algorithms: support vector machine (SVM), k-nearest neighbour (KNN), naive Bayes (NB), decision tree (DT), and random forest (RF); and deep learning techniques. The performance of the systems built are presented and compared. The SVM was found to be the best learning algorithm for the ECG data, while RF was the best for the PPG data. The proposed database is available to other researchers. Full article
(This article belongs to the Special Issue Machine Learning in Mathematical and Computational Biology)
►▼ Show Figures

Figure 1

20 pages, 1490 KiB  
Article
Investigating Shared Genetic Bases between Psychiatric Disorders, Cardiometabolic and Sleep Traits Using K-Means Clustering and Local Genetic Correlation Analysis
by Gianpaolo Zammarchi, Claudio Conversano and Claudia Pisanu
Algorithms 2022, 15(11), 409; https://doi.org/10.3390/a15110409 - 3 Nov 2022
Viewed by 1830
Abstract
Psychiatric disorders are among the top leading causes of the global health-related burden. Comorbidity with cardiometabolic and sleep disorders contribute substantially to this burden. While both genetic and environmental factors have been suggested to underlie these comorbidities, the specific molecular underpinnings are not [...] Read more.
Psychiatric disorders are among the top leading causes of the global health-related burden. Comorbidity with cardiometabolic and sleep disorders contribute substantially to this burden. While both genetic and environmental factors have been suggested to underlie these comorbidities, the specific molecular underpinnings are not well understood. In this study, we leveraged large datasets from genome-wide association studies (GWAS) on psychiatric disorders, cardiometabolic and sleep-related traits. We computed genetic correlations between pairs of traits using cross-trait linkage disequilibrium (LD) score regression and identified clusters of genetically correlated traits using k-means clustering. We further investigated the identified associations using two-sample mendelian randomization (MR) and tested the local genetic correlation at the identified loci. In the 7-cluster optimal solution, we identified a cluster including insomnia and the psychiatric disorders major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). MR analysis supported the existence of a bidirectional association between MDD and insomnia and the genetic variants driving this association were found to affect gene expression in different brain regions. Some of the identified loci were further supported by results of local genetic correlation analysis, with body mass index (BMI) and C-reactive protein (CRP) levels suggested to explain part of the observed effects. We discuss how the investigation of the genetic relationships between psychiatric disorders and comorbid conditions might help us to improve our understanding of their pathogenesis and develop improved treatment strategies. Full article
(This article belongs to the Special Issue Machine Learning in Mathematical and Computational Biology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop