Call for Papers and Posters Personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine  Pacific Symposium on Biocomputing Session January 4-8, 2015 Fairmont Orchid Resort, Kohala Coast The Big Island of Hawaii, U.S.A.

Motivation      

Genome sequencing and large-scale molecular and systemic phenotyping are already available for large patient studies, and will soon become routinely available for all patients. Additionally, consumer sensors enabling the “quantified self” have now made it possible to collect even richer, individual level, high- dimensional, multi-scale data--never before as cheap and accessible. This data deluge is setting the stage for rapid advances in personalized medicine, enabling better disease classification, more precise treatment, and improved screening leading to disease prevention. The increasingly rich repertoire of molecular and cellular data leads not only to the identity and structure of disease-related pathways but also to the identification of disease subtypes, their genetic underpinnings, possible drug targets and repositioning of drugs to these new targets. Key to realizing the promises of personalized medicine are robust computational approaches to handle a wide variety of problems including: hidden structure, missing data, data heterogeneity, massive sample sizes to detect associations with rare variants, feature selection over many mostly irrelevant features, noise, and the problem of multiple testing, among others.

Recent work illustrates the scope and complexity of this exciting new field. For example, when relating genotype to phenotype via genome-wide association studies, population structure and family relatedness can reduce power and cause spurious association. When assaying traits from clinical samples, cellular heterogeneity has been shown to confound results of naïve analyses, but also to reveal novel insights about the disease. Additionally, many phenotypes of interest are not independent but instead coupled by regulatory, or other factors. Hypothesizing and inferring the corresponding hidden factors, such as cell type or transcription factor activity has been shown to shed light on data sets that previously revealed little insights. There is also great potential to guide treatment by modeling genotype-dependent environmental effects on medical phenotypes by leveraging longitudinal multi-omics data, as well as leveraging and modeling data from very large sets of electronic medical records. Additionally, there is increasing activity in the implementation of personalized medicine in clinical settings reporting new data, perspectives, and challenges that will inform future efforts to implement personalized medicine at the bedside. In spite of this recent progress, further advances in statistical modeling and machine learning combined with informed clinical insights are still needed to realize the promise of personalized medicine computationally-informed therapy.

Recent breakthroughs in genome editing technologies hold promise to revolutionize and accelerate efforts to improve modeling and prediction of multi-scale biological systems. Technologies such as those based on CRISPR/Cas9 offer powerful and precise new means to systematically interrogate and perturb genome biology. Precise genome editing technologies offer unique methods and data that may be incorporated into a next generation of computational approaches for personalized medicine. For example, precise genomic editing, profiling, and computational modeling of patient-derived iPSC stem cells could form the basis of novel diagnostics.

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