ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine最新文献

Genome-wide association studies (GWAS) analyze genetic variation (SNPs) across the entire human genome, searching for SNPs that are associated with certain phenotypes, most often diseases, such as breast cancer. In GWAS, we seek a ranking of SNPs in terms of their relevance to the given phenotype. However, because certain SNPs are known to be highly correlated with one another across individuals, it can be beneficial to take into account these correlations when ranking. If a SNP appears associated with the phenotype, and we question whether this association is real, the extent to which its neighbors (correlated SNPs) also appear associated can be informative. Therefore, we propose CollectRank, a ranking approach which allows SNPs to reinforce one another via the correlation structure. CollectRank is loosely analogous to the well-known PageRank algorithm. We first evaluate CollectRank on synthetic data generated from a variety of genetic models under different settings. The numerical results suggest CollectRank can significantly outperform common GWAS methods at the cost of a small amount of extra computation. We further evaluate CollectRank on two real-world GWAS on breast cancer and atrial fibrillation/flutter, and CollectRank performs well in both studies. We finally provide a theoretical analysis that also suggests CollectRank's advantages.

We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.

Computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting protein-protein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://einstein.cs.iastate.edu/DockRank/.

We have developed a novel tool for visualizing and analyzing multiple collinear genomes. Unlike previous genome browsers and viewers, ours allows for simultaneous and comparative analysis. Our browser is web-based and provides intuitive selection and interactive navigation about features of interest. Dynamic visualizations adjust to scale and data content making analysis at variable resolutions and of multiple data sets more informative. Our tool illustrates genome-sequence similarity through a mosaic of intervals representing local phylogeny, subspecific origin, and haplotype identity. Comparative analysis is facilitated through reordering and clustering of tracks, which can vary throughout the genome. In addition, we provide local phylogenetic trees as an alternate visualization to assess local variations. We demonstrate our genome browser for an extensive set of genomic data sets composed of almost 200 distinct mouse strains.