Russ Altman's Translational Bioinformatics Year in Review

A few weeks ago the 2014 AMIA Translational Bioinformatics Meeting (TBI) was held in beautiful San Francisco.  This meeting is full of great science that spans the divide between molecular and clinical research, but a true highlight of this meeting is the closing keynote, traditionally given by Russ Altman.  Each year, he highlights the most exciting articles/publications in translational bioinformatics, and presents them all in a tidy, entertaining talk.  You can find his blog here, along with a link to his slide deck.  

To make accessing the papers a bit easier, here is a list of links.

Controversies:

FDA and 23andMe

Lior Pachter's "Network Nonsense"

Inconsistency in Large Pharmacogenomic Studies

Clinical Genomics:

Warfarin Dosing with Pharmacogenomics

Clinically Actionable Genotypes in Patients with Premptive Pharmacogenomic Testing

Genic Intolerance to Functional Variation 

Estimating the Relative Pathogenicity of Human Genetic Variants

Analyzing the Incidentalome

Whole Genome Sequencing in Support of Wellness and Health Maintenance

Drugs:

Manual Curation of 88,000 Articles for Drug-Disease and Drug-Phenotype Interactions

Mining the Druggable Genome

Pathway-based Drug Screening Strategy

Identification of Proteins that Elicit Drug Side Effects

Network-Assisted Prediction of Potential Drugs for Addiction

Drug Repositioning of Antidepressants as Inhibitors of Small Cell Lung Cancer

Combinatorial Therapy Discovery Using Mixed Integer Linear Programming

The Druggable Genome

Identifying Drug Targets using Subtractive Genome Analyses

Genetic Basis of Disease:

Deleterious Variants in Mendelian Loci Contribute to Complex Disease

Comparison of PheWAS and GWAS data

Coherent Functional Modules Improve Transcription Factor Target Identification and Disease Association

A Disease-Phenotype Knowledge Base: Extracting Disease-Manifestation Relationships

A Common Rejection Module for Acute Rejection Across Multiple Organs

Network Models of GWAS Uncover Topological Centrality of Protein Interactions in Complex Disease

Emerging Data Sources:

A Network-Based Method for Analysis of lncRNA-Disease Associations

Lineage Structure of the Human Antibody Repertoire in Response to Influenza Vaccination

An Integrated Clinico-Metabolomic Model for Prediction of Death in Sepsis

Meta-Analyses of Studies of the Human Microbiota

PhenDisco: Phenotype Discovery in DBGaP

Comorbidity Clusters in Autism Spectrum Disorders

Network-based Analyses of Vaccine-Related Associations

Mice:

Knockouts Model the 100 Best-Selling Drugs

Mouse Model Phenotypes Provide Information About Human Drug Targets

Genomic Responses in Mouse Models Poorly Mimic Human Inflammatory Disease

The Scientific Process:

Atypical Combinations and Scientific Impact

Protein Interactions and Complex Disease

Quantifying Long-Term Scientific Impact

The Yale University Open Data Access Project

Evidence of Community Structure in Biomedical Research Grant Collaborations

Odds & Ends:

A Haplotype-Resolved Genome and Epigenome of the Aneuploid HeLa Cancer Cell Line

A Social Network of Hospital Acquired Infection Build from EMR Data

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

How Do You Feel?  Your Computer Knows

Simulation of Repetitive Diagnostic Blood Loss and Onset of Iatrogenic Anemia

Unsuck your writing

I recently found this little gem of a web app that analyzes the clarity of your writing. Hemingway highlights long, complex, and hard to read sentences. It also highlights complex words where a simple one would do, and highlights adverbs, suggesting you use a stronger verb instead. It highlights passive voice (bad!), and tells you the minimum reading grade level necessary to understand your writing.

When I pasted in some text from an abstract I submitted to ASHG years ago it showed me just how terrible and difficult to understand my scientific writing really is. My abstract text, which should have been hard-hitting and easy to understand at a glance, required a minimum grade 20 reading level. The majority of my 14 sentences were very hard to read and littered with too many adverbs, complicated words, and several uses of passive voice. (I still got a talk out of the submission, so maybe we as scientists enjoy reading tortuous verbiage...).

It looks like a desktop version is in the works, but the web app seemed to work fine, even for a 100,000-word manuscript I tried.

http://www.hemingwayapp.com/