Big data and network biology 2015

Abstract

Recently, biology has become a data intensive science because of huge datasets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. In molecular biology, the list of components at the genome, transcriptome, proteome, and metabolome levels is gradually becoming complete and well-known to scientists. However, it is not holistically known how these components interact with each other to grow and maintain and reproduce life at different phases, in different environments, or with different challenging conditions. Networks at the molecular level are constructed to understand and explain processes and subprocesses of the cell. New tools and algorithms are being continuously developed for the purpose of handling and mining big biological data and networks aiming to serve humanity by developing smart health care systems, new generation medical tests, drugs, foods, fuel, materials, sensors, and so on.Overall, this improves the understanding of the cell or in other words the life as a system.Therefore, the range of topics under big data and network biology is extensive and the present special issue is not a comprehensive representation of the subject. Nonetheless, the articles selected for this special issue represent versatile topics concerning the title that we have the pleasure of sharing with the readers. The review paper “A Glimpse to Background and Characteristics of Major Molecular Biological Networks” focuses on biological background and topological properties of gene regulatory, transcriptional regulatory, protein-protein interaction, and metabolic and signaling networks. Versatile information contained in this article is helpful to facilitatea comprehensive understanding and to conceptualize the foundation of network biology. The paper titled “METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text” discusses a method for identifying transporter-substrate pairs by text mining and applied it to human transporter annotation sentences collected from UniProt database.The substrates of a transporter are not only useful for inferring function of the transporter, but also important in discovery of compound-compound interactions and reconstruction of metabolic pathways. Volatile organic compounds (VOCs) play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems.The paper titled “Development and Mining of a Volatile Organic Compound Database” discusses creation of a new VOC database by collecting information scattered in scientific literature and analyzed the accumulated data to showrelations between biological functions and chemical structures ofVOCs. This work also shows that VOC based classification of microorganisms is consistent with their classification based on pathogenicity. When inconsistent policies are applied to hospital computer systems, it can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. The paper “EMRlog Method for Computer Security for Electronic Medical Records with Logic and Data Mining” presents a new method named EMRlog for computer security systems in hospitals based on two kinds of policies, that is, directive and implemented policies.