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Recent advances in brain imaging and high throughput genotyping techniques enable new approaches to study the influence of genetic variation on brain structure and function. However, major computational challenges are bottlenecks for comprehensive joint analysis of these high-dimensional image and genomic data. We report our initial progress in developing an imaging genomic browsing system for integrated...
We investigate the problem of predicting protein-protein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validation-based approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the...
Although Ordinary Differential Equations (ODEs) have been used to model Genetic Regulatory Networks (GRNs) in many previous works, their steady-state behaviors are not well studied. However, a phenotype corresponds to a steady-state gene expression pattern and steady-state analysis of GRNs can provide valuable information on the stability of the GRNs, insights into cellular regulatory mechanisms underlying...
Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets....
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we...
Large-scale genome analysis and drug discovery require an automated prediction method for protein subcellular localization, and Support Vector Machines (SVMs) effectively solve this problem in a supervised manner. However, the protein subcellular localization datasets obtained from experiments always contain outliers, which can lead to poor generalization ability and classification accuracy. To address...
Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. Computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane...
Metagenomics is to study microorganisms by directly extracting and cloning their DNAs from the environment without lab cultivation or isolation of individual genomes. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads...
This study evolves agents to play iterated prisoners dilemma with choice and refusal. The choice and refusal mechanism causes the agents to self-organize social networks. We then apply a novel technique for inducing a pseudometric on the space of networks using diffusion characters to analyze the resulting social networks, and create an exploratory taxonomy of the social networks. The taxonomy agrees...
This paper presents a new algorithm for local alignment search which has less computational complexity than the Smith-Waterman algorithm. Increasing the accuracy of sequence matching and reducing computational complexity is a grand challenge to bioinformatics computing. The proposed Algorithm is a more competent local alignment searching Algorithm since it reduces running time well enough to search...
Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published...
Identification of interactions between molecular features (e.g. mutation, gene expression change) and gross phenotypes in diseases and other biological processes is one of the important challenges in genomic research. Popular approaches such as GSEA are limited to hypothesis tests of bivariate association. However, a specific phenotype is often dependent upon multiple molecular features. It is thus...
Microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica...
Recent years have seen a growing interest in the incorporation of protein-protein interaction (PPI) networks to support functional genomic research. Often a default depth is assumed by network inference software. This case study considers the impact of network depth on the analysis of PPI networks using seven proteins known to be relevant to heart failure as inputs into the analysis. This paper analyses...
Identification of the most successful strategy for applications in tissue engineering is often confusing, with a wide variety of options and variables available, that can fit into an ideal graft or scaffold. The complexity of the problem is multifold in application of grafts for regeneration of peripheral nerve injuries, with many variables that affect the regeneration process and thereby the success...
The following topics are dealt with: artificial intelligence and machine learning in bioinformatics; RNA design, RNA secondary structure prediction algorithms; sequence and phylogenetic analysis; genetic algorithm; DNA; protein and RNA structure prediction, folding, and docking; artificial neural network; toxins; protein-protein interactions; gene finding and microarray analysis; very large biological...
Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors...
Tracking the patella movement trajectory during the bending process of the knee is one essential step to knee pain diagnosis. In order for tracking patella, correct segmentation of the femur and patella from the axial knee MR image is indispensable. But the strong adhesion of the soft tissue around femur and patella, the gray-level similarities of adjacent organs, and the non-uniform gray intensity...
Protein structure similarity and classification methods have many applications in protein function prediction and associated fields (e.g. drug discovery). In this paper, we propose a new protein structure representation method enabling fast and accurate classification. In our approach, each protein structure is represented by number of vectors (based on histogram of distances) equivalent to the number...
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