BigLS 2015 - 2015 ACM International Workshop on Big Data in Life Sciences

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.
The focus of this year edition is on scientific workflows for Big Data analytics, including workflows design, optimization, management and execution in HPC and cloud environments.
In addition, papers catering to the following broad themes are also welcome:
Scalable algorithms and techniques for Big Data analytics in molecular biology.
Statistical and integrative approaches to Big Data biology.
Emerging Machine Learning and AI techniques for Big Data biology.
High performance computing methods and software for Big Data biology.
Software and hardware foundations for managing Big Data in biomedical informatics.
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.