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atSNP Search

atSNP Search is a web tool that evaluates the impact of single nucleotide polymorphisms (SNPs) on transcription factor binding (TF) in silico. It statistically quantifies whether any given SNP from the dbSNP Build 144 is likely to lead to gain and/or loss of function for binding of any TF from existing TF binding profile databases.

CPCP Seminar: Friends Don't Let Friends Use Black-Box Models: The Importance of Intelligibility in Machine Learning for Healthcare Seminar Video

Talk by Rich Caruana PhD, Microsoft Research

CPCP Seminar: The Bounty of the Commons Seminar Video

Casey Greene, PhD University of Pennsylvania Abstract: This is an exciting time in biomedical data science. It is now possible to collect substantial information about individuals and their encounters with health care. Our ultimate goal is to integrate this data, along with data and findings from those engaged in basic science, to identify new opportunities to improve health. Broad data sharing will further our progress towards this goal. However, data sharing poses both cultural and technological challenges. I'll discuss our work to address technical issues, including analysis approaches that lift techniques from the field of software engineering and data sharing approaches that employ deep generative neural networks. I'll also touch on our work to shift cultures, including the research parasite and research symbiont awards (applications for each due Sept 30!).

Anxiety-related experience-dependent white matter structural differences in adolescence: A monozygotic twin difference approach

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, L2-consistency and Neuroscience Applications

Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging

Machine learning consensus scoring improves performance across targets in structure-based virtual screening

Pharmacovigilance via baseline regularization with large-scale longitudinal observational data

A review of active learning approaches to experimental design for uncovering biological networks

CPCP 2017 Retreat: Phenotype Models for Breast Cancer Screening Symposium Video

Talk by Beth Burnside and Ming Yuan

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