This presentation will introduce BEL, an open-standards-based language that is human-friendly, composable, aggregatable and computable which solves some of the bottlenecks facing biology regarding re-usable and verifiable knowledge. It allows many different uses in data analytics that can incorporate prior-knowledge such as causal reasoning, Bayesian networks, deep-learning, and heat diffusion algorithms.

This presentation will showcase how data aggregation can be used in (eco)toxicology. Specifically, the talk will highlight how knowledge aggregation can be used to construct computable networks that describe chemically-induced developmental cardio and neurotoxicity in zebrafish larvae, and how the networks can be integrated with gene expression measurements to predict cardiac and neural toxicity after chemical exposure.

This presentation will highlight one use case of a package developed for analysis of high content screening studies in an industry setting and released to Bioconductor for further use by the scientific community.

This presentation will discuss two crowdsourcing efforts/challenges that aim at benchmarking methods used in metagenomics, namely CAMI and sbv IMPROVER.

INTERVALS, a platform enabling transparent data sharing, will be presented during the session as a particularly valuable source of varied and large datasets for computational scientists.

With the unprecedented growth in techniques to measure, mine and model complex systems in nature and life, the possibilities for novel scientific discoveries are expanding exponentially. This talk will explore the interplay of omics-scale measurement techniques, powerful machine learning models, novel computational platforms powered by high-quality and annotated data in driving the next generation engine of science, with specific focus on biology and medicine.

The panel discussion should enable the participants and speakers to further elaborate on the topics of reproducibility, transparency, and the use of crowdsourcing and citizen science.