David Heckerman

TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.

TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.

TL;DR: The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases, and a large hexanucleotide repeat expansion in the first intron of C9ORF72 is shown.

TL;DR: A new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping and takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows for substantially increase the computational speed and reliability of the results.