Transcriptional networks inferred from molecular signatures of breast cancer.

Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes.

Towards predicting protein-protein interactions in novel organisms.

Machine learning methods are often used to predict Protein-Protein Interactions (PPI). It is common to develop methods using known PPI from well-characterised reference organisms, drawing from that organism data for inferring a predictive model and evaluating the model. We present evidence that this practice does not give a meaningful indication of the model's performance on genetically distinct organisms.

Predicting dire outcomes of patients with community acquired pneumonia.

Community-acquired pneumonia (CAP) is an important clinical condition with regard to patient mortality, patient morbidity, and healthcare resource utilization. The assessment of the likely clinical course of a CAP patient can significantly influence decision making about whether to treat the patient as an inpatient or as an outpatient. That decision can in turn influence resource utilization, as well as patient well being.

Machine-learning techniques for macromolecular crystallization data.

Systematizing belief systems regarding macromolecular crystallization has two major advantages: automation and clarification. In this paper, methodologies are presented for systematizing and representing knowledge about the chemical and physical properties of additives used in crystallization experiments. A novel autonomous discovery program is introduced as a method to prune rule-based models produced from crystallization data augmented with such knowledge.