The ultimate goal of functional genomics is to elucidate the function of all the genes in the genome. However, the current notions of function are crafted for individual proteins. The degree to which they can scale to the genomic level is not clear. In this paper, we review the diverse approaches to functional classification, focusing on their ability to meet this challenge of scale. Our review emphasizes a number of key parameters of the systems: their accuracy, comprehensiveness, level of standardization, flexibility, and support for data mining. We then propose an approach that synthesizes a number of the promising features of the existing systems. Our approach, which we call a function grid, is based on the notion of defining a protein’s function through molecular interactions—specifically, in terms of its probability of interaction with various ligands, the list of which can be expanded infinitely. To illustrate how our function grid can be used in genome-wide prediction of function, we construct a grid of yeast genes; combine it with other genomic information, including sequence features, structure, subcellular localization, and messenger ribonucleic acid expression; and then use decision trees and support vector machines to predict deoxyribonucleic acid binding. Keywords—Function, grid, interaction, ontology, proteome. I.

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Abstract—This paper provides an overview of the application of evolutionary algorithms in certain bioinformatics tasks. Different tasks such as gene sequence analysis, gene mapping, deoxyribonucleic acid (DNA) fragment assembly, gene finding, microarray analysis, gene regulatory network analysis, phylogenetic trees, structure prediction and analysis of DNA, ribonucleic acid and protein, and molecular docking with ligand design are, first of all, described along with their basic features. The relevance of using evolutionary algorithms to these problems is then mentioned. These are followed by different approaches, along with their merits, for addressing some of the aforesaid tasks. Finally, some limitations of the current research activity are provided. An extensive bibliography is included. Index Terms—Biocomputing, data mining, evolutionary algorithm, molecular biology, soft computing. I.

Complex computer simulations are a class of applications that demands high performance processing power in order to be realized in a feasible time. To achieve this processing power, networks composed of non-dedicated machines are increasingly being investigated. An efficient scheduling scheme is one of the most important issues to make a better use of these resources. In this paper we present an architecture for scheduling complex computer simulations aimed at heterogeneous non-dedicated machines which relies on information provided by the models that are being simulated. Furthermore, a case study demonstrates how the proposed architecture can assist in the execution of complex simulations applied to the protein structure prediction problem, which is one of the most important current challenges in structural bioinformatics. 1.

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In a normal microarray classification problem there will be many genes, on the order of thousands, and few samples, on the order of tens. This necessitates a massive feature space reduction before classification can take place. While much time and effort has gone into evaluating and comparing the performance of different classifiers, less thought has been spent on the problem of efficient feature space reduction. There are in the microarray classification literature several widely used heuristic feature reduction algorithms that will indeed find small feature subsets to classify over. These methods work in a broad sense but we find that they often require too much computation, find overly large gene sets or are not properly generalizable. Therefore, we believe that a systematic study of feature reduction, as it is related to microarray classification, is in order. In this thesis we review current feature space reduction algorithms and propose a new, mixed model algorithm. This mixed-modified algorithm uses the best aspects of the filter algorithms and the best aspects of the wrapper algorithms to find very small yet highly discriminant gene sets. We also discuss methods to evaluate alternate, ambiguous gene sets. Applying our new mixed model algorithm to several published datasets we find that our new algorithm outperforms current gene finding methods.

this paper, we review the diverse approaches to functional classification, focusing on their ability meet this challenge of scale. Our review emphasizes a number of key parameters of the systems: their accuracy, comprehensiveness, level of standardization, flexibility, and support for data mining. We then propose an approach that synthesizes a number of the promising features of the existing systems. Our approach, which we call a function grid, is based on the notion of defining a protein's function through molecular interactions ---specifically, in terms of its probability of interaction with various ligands, the list of which can be expanded infinitely. To illustrate how our function grid can be used in genome-wide prediction of function, we construct a grid of yeast genes; combine it with other genomic information, including sequence features, structure, subcellular localization, and messenger ribonucleic acid expression; and then use decision trees and support vector machines to predict deoxyribonucleic acid binding

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Bioinformatics – exploring current scientific literature for emerging topics

Abstract not found