Breakthroughs in genomics data integration for predicting clinical outcome

Breakthroughs in genomics data integration for predicting clinical outcom

Information-theoretic classification of SNOMED improves the organization of context-sensitive excerpts from Cochrane Reviews

The emphasis on evidence based medicine (EBM) has placed increased focus on finding timely answers to clinical questions in presence of patients. Using a combination of natural language processing for the generation of clinical excerpts and information theoretic distance based clustering, we evaluated multiple approaches for the efficient presentation of context-sensitive EBM excerpts

Desistance from Sexual Offending: A Policy and Research Agenda Whose Time Has Come

For the past three decades or so, criminal justice policies have been enacted under the assumption that individuals who have been convicted of a sex offense are life course persistent sex offenders. In that context, research has been heavily focused on the assessment of risk and the prediction of sexual recidivism. Simultaneously, little to no attention has been given to the majority of individuals convicted of sex offenses who are not arrested or convicted again.Researchers have witnessed a growing gap between scientific knowledge and the sociolegal response to sexual violence and abuse. The current legal landscape carries important social implications and significant life course impact for a growing number of individuals. More recently, theoretical and research breakthroughs in the study of desistance from crime and delinquency have been made that can help shed some light on desistance from sex offending. Desistance research, in the context of sex offending, however, represents serious theoretical, ethical, legal, and methodological challenges. To that end, this article introduces a special issue exploring current themes in desistance research by examining the life course of individuals convicted of a sexual offense while contextualizing their experiences of desistance.No Full Tex

The Development and Statistical Testing of a Nascent Organization Structure Sequence Model

This study identified episodic stages of growth used by nascent hyper-growth firms. Based on the literature, an 11-stage hypothetical episodic Nascent Organization Structure Sequence (NOSS) model was postulated. Sixty-two Inc-500 fastest-growing "gazelle" entrepreneurs identified which of the 11 stages they used or would use to build their business. One-sample chi-square analysis per NOSS stage found that nine of the growth stages were identified as significant (p < .05), resulting in a revised Verified Nascent Organization Structure Sequence (VNOSS) model of high-growth, emerging organizational development. See Figure 1 for a list of the 11 NOSS stages, and Figure 2 for the resulting 9 VNOSS stages. The model contributes to the development of a research foundation that can aid entrepreneurs in changing their structures as they grow their businesses, as well as consultants who help them grow

A Single-Subject Method to Detect Pathways Enriched With Alternatively Spliced Genes

RNA-Sequencing data offers an opportunity to enable precision medicine, but most methods rely on gene expression alone. To date, no methodology exists to identify and interpret alternative splicing patterns within pathways for an individual patient. This study develops methodology and conducts computational experiments to test the hypothesis that pathway aggregation of subject-specific alternatively spliced genes (ASGs) can inform upon disease mechanisms and predict survival. We propose the N-of-1-pathways Alternatively Spliced (N1PAS) method that takes an individual patient’s paired-sample RNA-Seq isoform expression data (e.g., tumor vs. non-tumor, before-treatment vs. during-therapy) and pathway annotations as inputs. N1PAS quantifies the degree of alternative splicing via Hellinger distances followed by two-stage clustering to determine pathway enrichment. We provide a clinically relevant “odds ratio” along with statistical significance to quantify pathway enrichment. We validate our method in clinical samples and find that our method selects relevant pathways (p < 0.05 in 4/6 data sets). Extensive Monte Carlo studies show N1PAS powerfully detects pathway enrichment of ASGs while adequately controlling false discovery rates. Importantly, our studies also unveil highly heterogeneous single-subject alternative splicing patterns that cohort-based approaches overlook. Finally, we apply our patient-specific results to predict cancer survival (FDR < 20%) while providing diagnostics in pursuit of translating transcriptome data into clinically actionable information. Software available at https://github.com/grizant/n1pas/tree/master