Call for Papers: Special Session at 2014 IEEE Symposium on Computational Intelligence in healthcare and e-health (IEEE CICARE 2014)


Big Data Analytic Technology for Bioinformatics and Health Informatics


Organizers: Dr. Xin Deng*, Dr. Donghui Wu**

*Chair and Primary Contact, Research Scientist, LexisNexis, Risk Solutions , Healthcare, Orlando, FL


**Alternate Contact:, Senior Director, Statistical Modeling, LexisNexis | Risk Solutions | Healthcare



The emerging fusion of Bioinformatics and Health Informatics has promoted research in target drug, personalized medicine, clinical decision support and population health management, etc. and collaborations among researchers in bioinformatics and health informatics and clinicians as well as data scientists. It also demands big data analytics incorporating latest advancement in computational intelligence, data mining, machine learning and statistical methodologies. It is worthwhile to hold this special session to provide a platform for professionals, researchers, clinicians, and data scientists to share opinions and exchange ideas, so as to facilitate fusion of Bioinformatics and Health Informatics academic and industry research, and the improvement in the quality of people's daily health and life activities as well as dig data analytics to support and promote such research activities.


Topic and Target Audience 


The field of Bioinformatics enhances the development of databases, algorithms, computational and statistical techniques and tools to solve a variety of practical problem, and provide a new point of view about rough and abstract biological data by analyzing and correlating genomic and proteomic information. As increasingly massive amounts of biological information, including genome sequences, protein sequences, gene expression data, becomes available, more efficient, sensitive, and specific big data analytic technology in Bioinformatics become critically in need. For example, terabytes or more of raw data are easily generated in next-generation sequencing experiments. Also, in biological and biomedical imaging process and analysis, large volumes of data are generated. Consequently, how to store, achieve, index, manage, learn, mine, and visualize the big data is clearly a challenge to the research community. Similar to Bioinformatics, the same rule of data driven science also works in Health Informatics. For the past decade, there have been a variety of efforts and progress from healthcare organizations and companies in digitizing, storing, manipulating medical data and saving healthcare costs with the help of advanced analytics. For instance, predictive analytic models and risk adjustment methodologies embedded within data analysis platforms allow insurance companies and healthcare organizations to predict the future costs for budgets and population health management, perform risk adjustment, develop the treatment guidelines, plan care management strategies, and measure physician performance. Big Data Analytics has become an emerging and vital problem in Health Informatics as well.

Moreover, the growing fusion of Bioinformatics and Health Informatics facilitates the development of big data analytic technology. For instance, the integration of genetic test results, patient-specific sequencing, expression profiling, tissue image data, clinical data in a patient medical record provides opportunities for personalized medicine, target drug research, and treatment effectiveness research, and in turn create new challenges for big data analytics from database design, data querying, data knowledge representation, to data analytics, and clinical decision support.


Target Audience


The goal of this session is to bring together practitioners, researchers, clinicians, and data scientists in the area of Bioinformatics and Health Informatics to share latest findings in the field, exchange ideas on how to improve the strategies, address real-world problems in Bioinformatics and Healthcare, and explore the intersections between Bioinformatics and Health Informatics and new research areas brought by advancement in big data analytics, data mining, machine learning and statistical learning.


List of Topics 


Papers from all areas of computational intelligence, data mining, predictive model, statistical and machine learning with applications in Bioinformatics and Health Informatics are welcome. Authors are invited to submit their papers in the following topics, but not limited to these topics:


·        Healthcare and healthcare delivery

·        Healthcare policy research

·        Healthcare outcomes research, monitoring and evaluation

·        Health Analytics and Informatics

·        Hospital Information System

·        Electronic Medical Record and Electronic Health Record

·        Population Health Management

·        Other areas related to healthcare

·        Protein structure prediction

·        Protein function analysis

·        Drug design

·        RNAseq and microarray gene expression data analysis

·        Gene regulatory network construction

·        Next-generation sequencing (NGS) analysis

·        Other areas related to proteomics and genomics

·        Other related areas with applications in big data technology




·        Paper submission:  15 July 2014, Midnight GMT

·        Decision:                05 Sept 2014

·        Final submission:   05 Oct 2014

·        Early Registration:  05 Oct 2014


Paper Submission

Please submit your paper via the IEEE SSCI 2014 on-line submission link accessed from: