Ning Lan

TL;DR: It is shown that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment, and less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal Growth in four of the tested conditions.

TL;DR: This work uses tree-based analyses and random forest algorithms to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation and identifies combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets.

TL;DR: This work developed a comprehensive set of data mining features for each protein, including several related to experimental progress and demonstrated in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features.

TL;DR: The discovery of 137 previously unappreciated genes in yeast through a widely applicable and highly scalable approach integrating methods of gene-trapping, microarray-based expression analysis, and genome-wide homology searching, which provides an effective supplement to current gene-finding schemes.

TL;DR: This paper focuses on the prediction of membership in protein complexes for individual genes, and recruits six different data sources that include expression profiles, interaction data, essentiality and localization information, which can be improved by combining all of them.