CS 229: Machine Learning, taught by Andrew Ng.
Automatically Detecting Banner Ads in Web Pages [PDF].
This paper describes AdZap, a Firefox browser plugin for detecting and blocking advertisements on web pages. AdZap uses a set of labeled training data collected from the user as input to a supervised learning algorithm. The trained algorithm then examines images embedded in HTML documents shown to the user and hides images classified as advertisements.
BMI 217: Translational Bioinformatics, taught by Atul Butte.
A Genome-Wide Association Study of Inbred Rat Strains [PDF].
Python source code available.
This paper describes a genome-wide association study conducted on various inbred strains of Brown Norway rat. The study used preexisting, publicly available data. Phenotype data collected by NBRP-Rat, Kyoto, was merged with genotype data collected by the European STAR consortium. Applying a stringent Bonferroni correction, no statistically significant results were found. Applying a more lenient criterion based on false discovery rate led to several hundred possibly significant correlations between SNPs and phenotypes. These findings were combined with Gene Ontology annotations from the Rat Genome Database to associate particular phenotypes with particular Gene Ontology terms. The associations suggest biological pathways and mechanisms that may give rise to the phenotypic variations observed between various strains of rats.
Boring page about the author