Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets

Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes

Impact of preoperative teaching on surgical option of patients qualifying for bariatric surgery.

BACKGROUND: During the last 5 years, the performance of bariatric operations has doubled via our outpatient obesity clinic. Currently, 52% of the patients presenting for weight loss are interested in bariatric surgery. Gastric banding and Roux-en-Y gastric bypass are the two laparoscopic procedures proposed. The aim of this study was to evaluate the impact of preoperative teaching on the patients' surgical option. METHODS: All the candidates for bariatric surgery were submitted to preoperative teaching and those between February 2001 and December 2002 are the subject of this study. The teaching consisted of 3 weekly interactive 2-hour sessions. During the first session, the patients were asked about the type of operation that they had in mind: gastric banding, gastric bypass, or not yet decided. The same questions were repeated at the end of the third session, with an additional possible answer: no surgery. RESULTS: 297 consecutive patients with a BMI >35 kg/m(2) with at least one severe co-morbidity, were submitted to preoperative teaching. 80% of the patients were women. Median age was 41 years. Before teaching, 68 patients (23%) were uncertain, 100 (34%) favored gastric banding, and 129 (43%) wanted a gastric bypass. After education, only 3 patients (1%) remained uncertain, 45 (15%) changed their surgical option, and 27 (9%) declined surgery. The proportion of patients opting for gastric banding decreased from 34% to 20%, whereas those electing bypass increased from 43% to 70%. CONCLUSIONS: Preoperative training provides an informed and better patient selection for bariatric surgery. It helps the patients understand the various surgical options, and makes their decision easier