The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi-Omics Data Integration

Dear Colleagues,

In the last two decades, there have been massive advancements in the generation of data in the field of bioinformatics, such as in high throughput technologies, which have resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. Integrative bioinformatics is a discipline of bioinformatics that focuses on problems of data integration. With the advent of sequencing technology, biology has become increasingly dependent on data generated at these levels, which together is called as “multi-omics” data. One of the main goals of this special issue is to explore the novel methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid in the prediction, diagnosis, and treatment of diseases, as well as to enlighten us with regard to disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. 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 the development of such techniques.

With this Special Issue, we envision providing a forum 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, either in the form of original research articles, reviews, or shorter perspective articles on all aspects related to the theme of “Integrative Bioinformatics Based Machine Learning Techniques ”. Articles with sound methodology and scientific practice are particularly welcomed. Relevant topics include, but are not limited to, the following:

• Machine learning;
• Feature selection;
• 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;
• System 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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