Machine Learning in Bioinformatics, Computational Biology and Biomedical Text Classification

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

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