BigLS - Big Data in Life Sciences

ACM International Workshop on Big Data in Life Sciences

In conjunction with the ACM Conference on Bioinformatics, Computational Biology and Health Informatics

Wednesday, September 9, 2015
Atlanta, GA


Call for Papers

The ever-growing volume and diversity of biological and biomedical data collections continues to pose new challenges and increasing demands on computing and data management. The inherent complexity of this Big Data forces us to rethink how we collect, store, combine and analyze it.

BigLS is a workshop series dedicated to the broad theme of Big Data in life sciences. The goal of the workshop is to bring together leading researchers and practitioners working on a diverse range of Big Data problems relating to biology and medicine, and engage them in a discussion about current Big Data problems, the state of computational tools and analytics, the challenges and the future trends within life sciences.

In addition, papers catering to the following broad themes are also welcome:

In addition to regular papers catering to the above spectrum of topics, we also invite "position papers" to highlight some of the grand challenge scientific problems from a biological standpoint, existing or emerging, that require Big Data analytics, along with related challenges and advances.

The workshop is devoted to promoting the highest standards in research and education. As a part of this mission, BigLS features keynote thematic presentations by recognized leaders and luminaries who significantly advanced the domain. It also hosts an educational session where students can interact with established researchers and principal investigators.

You can download this CfP:

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Keynote Address

The BigLS workshop is devoted to promoting the highest standards in research and education. As a part of this mission, BigLS features keynote presentations by recognized leaders and luminaries who significantly advanced the domain. This year keynote address will be delivered by Eva K. Lee - Director of the NSF-Whitaker Center for Operations Research in Medicine and HealthCare, Co-Director of the NSF I/UCRC Center for Health Organization Transformation, Distinguished Scholar in Health Systems in the Health System Institute at Georgia Tech/Emory University.

Machine Learning Framework for Classification in Medicine and Biology

Systems modeling and quantitative analysis of large amounts of complex clinical and biological data may help to identify discriminatory patterns that can uncover health risks, detect early disease formation, monitor treatment and prognosis, and predict treatment outcome. In this talk, we describe a machine-learning framework for classification in medicine and biology. It consists of a pattern recognition module, a feature selection module, and a classification modeler and solver. The pattern recognition module involves automatic image analysis, genomic pattern recognition, and spectrum pattern extractions. The feature selection module consists of a combinatorial selection algorithm where discriminatory patterns are extracted from among a large set of pattern attributes. These modules are wrapped around the classification modeler and solver into a machine learning framework. The classification modeler and solver consist of novel optimization-based predictive models that maximize the correct classification while constraining the inter-group misclassifications. The classification/predictive models 1)have the ability to classify any number of distinct groups; 2) allow incorporation of heterogeneous, and continuous/time-dependent types of attributes as input; 3) utilize a high-dimensional data transformation that minimizes noise and errors in biological and clinical data; 4) incorporate a reserved-judgement region that provides a safeguard against over-training; and 5) have successive multi-stage classification capability. Successful applications of our model to developing rules for gene silencing in cancer cells, predicting the immunity of vaccines, identifying the cognitive status of individuals, and predicting metabolite concentrations in humans will be discussed.

Eva K. Lee is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, and Director of the Center for Operations Research in Medicine and HealthCare, a center established through funds from the National Science Foundation and the Whitaker Foundation. The center focuses on biomedicine, public health, and defense, advancing domains from basic science to translational medical research; intelligent, quality, and cost-effective delivery; and medical preparedness and protection of critical infrastructures. She is a Distinguished Scholar in Health Systems, Health System Institute at Georgia Tech and Emory University. She is also the Co-Director of the Center for Health Organization Transformation, an NSF Industry/University Cooperative Research Center. Lee partners with hospital leaders to develop novel transformational strategies in delivery, quality, safety, operations efficiency, information management, change management and organizational learning. Lee graduated from Rice University with a degree in Computational and Applied Mathematics, and received NSF/NATO postdoctoral training in scientific computing. Her research focuses on mathematical programming, information technology, and computational algorithms for risk assessment, decision making, predictive analytics, knowledge discovery, and systems optimization. She has made major contributions in advances to medical care and procedures, systems bioinformatics, emergency response and medical preparedness, healthcare operations, and business transformation. A brief glimpse of Dr. Lee’s healthcare work can be found here.

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Travel grants

Thanks to the generous support from the NSF, the BigLS workshop will offer the travel grants for students and postdoctoral researchers from US academic institutions. These awards are independent from the main ACM BCB conference grants, and candidates can apply only for one of them. In order to apply candidates should submit before August 14 the following materials to Dr. Ananth Kalyanaraman <> (priority will be given to females and under-represented minorities):

  1. A statement letter from the applicant, that includes i) a brief summary of research area and achievements, ii) explains how applicant will benefit from the BigLS workshop. Candidates should demonstrate interest in Big Data problems related to life sciences.
  2. A supporting letter from the applicant's supervisor/advisor, including confirmation that the student is in good academic standing, confirmation how the BigLS scope is relevant to his/her research, and how applicant will cover potential expenses not covered by the award (e.g. per diem).
  3. Title of a paper/poster accepted for presentation at BigLS (if any).

At the minimum the award is expected to cover the entire ACM BCB conference registration and hotel lodging (two nights for the duration of the workshop). Based on funding availability, additional support will be provided to cover parts or whole of the airfare and lodging expenses for the remaining duration of the conference. All questions regarding the BigLS travel awards should be directed to Dr. Ananth Kalyanaraman. To avoid miscommunication (e.g. email being flagged as spam) please do not hesitate to contact Dr. Kalyanaraman again if you get no confirmation of your application within one day.

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Submission Guidelines

Submitted manuscripts should not exceed 10 pages in the ACM template on 8.5 x 11 inch paper. All submissions will be evaluated based on their originality, technical soundness, significance, presentation, and interest to the workshop attendees. All accepted papers of registered authors will be included in the ACM BCB proceedings published by the ACM Digital Library. Details for electronic submission can be found on the ACM BCB conference web portal. To submit you paper please use this link:

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Important Dates

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Workshop Co-chairs

Program Committee

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BigLS Archive

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