Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

/pdf/gene-set-enrichment-analysis-a-knowledge-based-approach-for-mbpti6qljw.pdf

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

http://www.math.tau.ac.il/~ybenja/MyPapers/benjamini_drai_elmer_BEHAVBRAINRES.pdf

Controlling the false discovery rate in behavior genetics research

Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.

Genome-wide association studies for common diseases and complex traits

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

The author explores the viability of a comparative approach to personality research. A review of the diverse animal-personality literature suggests that (a) most research uses trait constructs, focuses on variation within (vs. across) species, and uses either behavioral codings or trait ratings; (b) ratings are generally reliable and show some validity (7 parameters that could influence reliability and 4 challenges to validation are discussed); and (c) some dimensions emerge across species, but summaries are hindered by a lack of standard descriptors. Arguments for and against cross-species comparisons are discussed, and research guidelines are suggested. Finally, a research agenda guided by evolutionary and ecological principles is proposed. It is concluded that animal studies provide unique opportunities to examine biological, genetic, and environmental bases of personality and to study personality change, personality-health links, and personality perception.

From mice to men: what can we learn about personality from animal research?

The critical role of group studies in neuropsychology: comprehension regularities in Broca's aphasia.

http://www.tau.ac.il/~ilan99/pubs/2000_Statistical%20discrimination%20of%20natural%20modes%20of%20motion%20in%20rat%20exploratory%20behavior.pdf

Statistical discrimination of natural modes of motion in rat exploratory behavior.

SEE: a tool for the visualization and analysis of rodent exploratory behavior.

https://www.tau.ac.il/~ilan99/pubs/2001_Rats%20and%20mice%20share%20common%20ethologically%20relevant%20parameters%20of%20exploratory%20behavior.pdf

Dan Drai

Papers

Controlling the false discovery rate in behavior genetics research

The critical role of group studies in neuropsychology: comprehension regularities in Broca's aphasia.

Statistical discrimination of natural modes of motion in rat exploratory behavior.

SEE: a tool for the visualization and analysis of rodent exploratory behavior.

Rats and mice share common ethologically relevant parameters of exploratory behavior.