Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. Although it has been demonstrated in the literature that methods using protein-protein interaction data alone can successfully predict a large number of protein complexes, the incorporation of gene expression profiles could help refine the putative complexes and hence improve the accuracy of the computational methods. By combining protein-protein interaction data and microarray gene expression profiles, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network and then breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our extensive tests on three widely used protein-protein interaction datasets and comparisons with the latest methods for protein complex identification demonstrate the superior performance of our method in terms of accuracy, efficiency, and capability in predicting novel protein complexes. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.

The recent explosion in the number of clinical studies involving microarray data calls for novel computational methods for their dissection. Human protein interaction networks are rapidly growing and can assist in the extraction of functional modules from microarray data. We describe a novel methodology for extraction of connected network modules with coherent gene expression patterns that are correlated with a specific clinical parameter. Our approach suits both numerical (e.g., age or tumor size) and logical parameters (e.g., gender or mutation status). We demonstrate the method on a large breast cancer dataset, where we identify biologically-relevant modules related to nine clinical parameters including patient age, tumor size, and metastasis-free survival. Our method is capable of detecting disease-relevant pathways that could not be found using other methods. Our results support some previous hypotheses regarding the molecular pathways underlying diversity of breast tumors and suggest novel ones.

We improve on guided genome halving algorithms so that several thousand gene sets, each containing two paralogs in the descendant T of the doubling event and their single ortholog from an undoubled reference genome R, can be analyzed to reconstruct the ancestor A of T at the time of doubling. At the same time, large numbers of defective gene sets, either missing one paralog from T or missing their ortholog in R, may be incorporated into the analysis in a consistent way. We apply this genomic rearrangement distance-based approach to the recently sequenced poplar (Populus trichocarpa) and grapevine (Vitis vinifera) genomes, as T and R respectively.

In this paper we introduce a novel graph classification algorithm and demonstrate its efficacy in drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to create features capturing graph local topology. We design a novel graph kernel function to utilize the created feature to build predictive models for chemicals. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than 10 fold speed up.

Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.