Machine Learning in Bioinformatics, Computational Biology and Biomedical Text Classification
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 6502
Special Issue Editor
Interests: bioinformatics; text mining; biological domain knowledge based feature selection on gene expression data; microRNA; one-class
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The "big data challenge" currently facing biomedical sciences is that of determining how to analyze the large quantities of raw biological data, including genomic sequences, proteomic and metabolic measurements and transcriptomic and metagenomic profiles. Thus, the application of machine learning (ML) is becoming an important element in meeting the challenge of analysis and integration of different data types. ML is also becoming increasingly important for mining information in biomedical literature and electronic documents where text mining and classification are becoming more important.
In this Special Issue, we envision providing a primer for the application of machine learning to a wide variety of different data sets to demonstrate its utility in addressing these growing computational challenges. We invite your contributions (original research articles, reviews, or shorter perspective articles) on all aspects related to the theme of “Machine Learning in Bioinformatics, Computational Biology and Biomedical Text Classification”. Articles with sound methodology and scientific practice are particularly welcome. Relevant topics include, but are not limited to, the following:
- Machine learning;
- Feature selection;
- Computational biology;
- Biomedical text classification;
- Integrative analysis of biomedical data;
- Machine learning in bioinformatics integrated with biological domain knowledge;
- Integrative analysis of multi-omics;
- Deep learning approaches;
- Genomics;
- Proteomics;
- Systems biology;
- Gene expression analysis;
- Machine learning for biomedical data analysis;
- Computational modeling and data integration;
- Biomedical text mining and ontologies;
- Next-generation sequencing data analysis;
- Drug discovery;
- Single-cell sequencing data analysis;
- Microbiome and metagenomics;
- Machine learning and deep learning in image analysis.
Prof. Dr. Malik Yousef
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. Applied Sciences 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 2400 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
- Computational biology
- Text classification
- Genomics
- Biomedical text mining
- Integrative analysis of biomedical big data
- Electronic medical records
