The International Journal of Biomedical Data Mining (JBDM) is a scholarly open access, peer-reviewed, and fully refereed journal which publishes articles on valuable algorithms, methods and software tools encompassing the arena of data mining, knowledge discovery, data analysis and machine learning techniques and their applications in biomedical, healthcare and bioinformatics problems. Contributions are welcome from different disciplines such as computer science, engineering, statistics, biomedical informatics, general sciences and mathematics.
Articles presenting original research in the field, highlighting methodological aspects and providing experimental evidences on specific problems and all aspects of data mining applied to high-dimensional biological and biomedical data are expected for publishing in this periodical. This Journal acknowledges the inter-disciplinary nature of research in biological data mining and bioinformatics and provides a unified forum for researchers, scientists, students, policy makers to share the latest research and developments in this fast growing multi-disciplinary research area. Comprehensive review articles, short communications, technical notes and book and software reviews are also welcome.
The Journal is using Editor Manager System for smooth functioning of the peer review process and acceptance of any citable article depends on at least two independent outside reviewers comment followed by Editor's discretion.
Bioinformatics is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, and protein structures as well as molecular interactions.
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Biomedical involves the application of the natural sciences, especially the biological and physiological sciences, to clinical medicine. It is a discipline that advances knowledge in engineering, biology and medicine and improves the human health through cross disciplinary activities that integrate the engineering sciences with biomedical sciences and clinical practices.
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Computational biology is the application of computer science, statistics, and mathematics to problems in biology. Computational biology spans a wide range of fields within biology, including genomics/genetics, biophysics, cell biology, biochemistry, and evolution. Likewise, it makes use of tools and techniques from many different quantitative fields, including algorithm design, machine learning, Bayesian and frequents statistics and statistical physics.
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Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.
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Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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Epidemiology data represents the abundance of a particular pathogenic organism in a specific geographical region. Analysis of such data along with the impact of various important factors such as climatic and geographical factors, alteration of behavior of the pathogen and the host or the carrier of the pathogen is mandatory in such issues. Advanced computation and statistical means are the ways to analyze such important data and predict the future aspects of the disease epidemics.
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Data mining impacted the present era of information retrieval system from a heap of data. Irrespective of the data, advanced mining techniques provide the essence of the datasets and extract the pattern of data. Next generation sequencing techniques generated a large pool of information from the genomes of several important organisms. Analysis of the genome data through mining has provided a tool to us for retrieval of the true meaning of the information and also to reduce the noise in the generated information.
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Computational analyses gained momentum after the human genome project and it proved its importance in analyzing biological information. Apart from in vivo and in vitro methodologies in silico techniques are popular among the scientific community in generating reliable scientific information based on the requirement of data analysis.
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Medical devices are of different types, some are used for monitoring and some are used for diagnostics purposes. In both the cases they are useful and being used for the urgent medical need.
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Sequence analysis is a branch of Bioinformatics which encompasses the analysis of the generated genes, proteins or genome sequences belonging to any organism. Sequence analysis techniques uses mathematical modeling methods such as Hidden Markov Models (HMM), DSSP, position specific scoring matrices (PSSM) to analyze a pattern hidden in a particular sequence.
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Medical Informatics is defined by the use of informatics and associated applications for relevant medical purpose. This includes programming for medical devices or other biomedical purposes, development of relevant databases for general or medical use, specific informatics based pipe line development to solve a particular medical problem.
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Protein molecules are structural and functional component of a cellular environment. Estimation of the quantity of proteins has been a routine process in scientific community and academics. Sequencing of proteins unveiled the mysteries of proteins and aided in understanding the complex structural and functional phenomena of protein networks and cascading events. Several methods are used for protein sequencing which contains enzyme based methods, mass spectrometry based methods and automated sequencing methods.
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System biology represents the analysis of an organization cascading physiological or biochemical events through experimentation and computational methods. System biology includes large scale experimentation or computational techniques including system representation in an in silico framework and analyzing and visualizing a real time cellular event which may use system biology mark up (SBML) language or other sophisticated and advanced technology.
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Structural prediction of protein molecules involves analysis of the secondary, tertiary and quaternary component of a protein. Stereo chemical assessment and other aspect can be evaluated in a cost effective manner.
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Proteomics refers to the analysis of the whole proteome for a particular organism. This includes experimental and theoretical techniques.
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Biomedical data mining refers to the analysis of the data generated for a solution of a biomedical problem. Advanced computational methodologies are in general applied for data mining purpose.
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Biostatistics is an interdisciplinary branch of mathematics and statistics where problems are considered from biology. Application of statistical measures is crucial for several important decision and classification purposes. Clinical informatics applies the advanced statistical implementations for solving the issues based on ample amount of clinical data generated for different diseases.
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*Unofficial 2015 Impact Factor was established by dividing the number of articles published in 2013 and 2014 with the number of times they are cited in 2015 based on Google search and the Scholar Citation Index database. If 'X' is the total number of articles published in 2013 and 2014, and 'Y' is the number of times these articles were cited in indexed journals during 2015 then, impact factor = Y/X