Machine Learning and Data Analysis in Bioinformatics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3476
Special Issue Editor
2. Department Genetics, University of Malaga, 29010 Malaga, Spain
Interests: unsupervised machine learning methods; topological data analysis and deep learning; hidden patterns; causal relationships from high-dimensional biological data
Special Issue Information
Dear Colleagues,
The field of biological research is generating vast amounts of high-dimensional data that require sophisticated analytical tools to uncover the hidden patterns and causal relationships that underlie biological processes. Unsupervised machine learning methods, such as clustering and dimensionality reduction techniques, have been widely used to identify subgroups within large datasets and to visualize complex data structures.
Recently, topological data analysis (TDA) has emerged as a powerful tool to analyze high-dimensional data and extract meaningful features. By focusing on the shape and structure of the data, rather than just the individual data points, TDA can identify topological features and structures in the data that traditional statistical methods may miss.
Deep learning techniques, such as neural networks and convolutional networks, have also shown great promise in identifying subtle patterns and relationships within large datasets. These methods can learn complex representations of the data and can be used to make accurate predictions based on high-dimensional inputs.
In this Special Issue, we invite researchers to submit their original research articles, reviews, and perspectives on unsupervised machine learning methods, topological data analysis, and deep learning in the context of biological data. We encourage submissions that focus on novel applications, methodologies, and algorithmic developments in these areas, as well as studies that showcase the potential of these techniques for advancing our understanding of complex biological systems.
Dr. Ian Morilla
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- unsupervised machine learning
- data analysis
- deep learning
- hidden patterns
- causal relationships
- high-dimensional data
- biological research
- network analysis
- complex data structures
