Big Data in Omics Science: Challenges and Opportunities

Dear Colleagues,

In recent years, Big Data has become a very popular word among people over the world. From social networks, e-commerce, finance, up to space, data are collected to infer nontrivial trends, as well as hidden knowledge. Nevertheless, people are less aware of the invasion of Big Data in biology, especially in omics science.

Technological advancements in the field of omics data have led to the production of a considerable volume of diverse data present in many formats, including gene expression and protein expression, DNA methylation, protein–protein interaction (PPI) networks, biological pathways, high-resolution images, and so on. Thus, omics data are big, and their scale has already been well beyond petabyte or even exabyte.

The challenges to face now are how to handle the complexity, the heterogeneity, and the storage of these immense collections of data, to better elucidate the mechanism underlying the work of the cellular machinery.

Parallel and distributed computing can be employed to provide the researcher with new scalable bioinformatics software tools and techniques, as well as to upgrade existent bioinformatics software tools, combined along with machine learning, data mining, and statistical analysis capabilities, enabling the efficient analysis, integration, and storage of biological data, as well as translating these considerable amounts of data into actionable knowledge, which can be employed to better understand how a genome is organized.

Further, bioinformatics tools could be made available as cloud services, making it possible to further increase the spread and utilization of advanced bioinformatics tools, even in small research centers. Thus, new bioinformatics software tools exploiting high-performance computing, along with the use of machine learning algorithms, can speed up the analysis of complex living organisms that cannot be entirely appreciated by merely analyzing individual components.

Dr. Giuseppe Agapito
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. Big Data and Cognitive Computing 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 1800 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.

• big data
• cloud computing
• parallel computing
• microarray data analysis
• biological network analysis
• data mining
• network analysis