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ieee symposium on Differential Evolution (SDE)

作者： Kalegari, Diego H. Lopes, Heitor S. Univ Tecnol Fed Parana Bioinformat Lab Curitiba Parana Brazil

ISBN: (纸本)9781467358736

Protein structure prediction (PSP) is a well-known problem in bioinformatics. Identifying protein native conformations makes it possible to predict its function within the organism. Knowing this also helps the development of new drugs and the comprehension of some biological mechanisms. During years some techniques have been developed for this purpose but, due to their high cost, it is necessary to use simplified models of protein structures. However, even the simplest models, with low biological plausibility, are excessively complex from the computational point of view. This paper reports the application of Differential Evolution (DE) to solve the PSP problem using a Toy Model (also known as the AB Model) in both 2D and 3D to represent the protein structure. This work presents two different versions of the DE algorithm (basic and adaptive) using a parallel architecture (master-slave) based on Message Passing Interface in a cluster. Some special operators for DE were developed: explosion and mirror mutation. All tests executed in this work used four benchmark sequences, ranging from 13 to 55 amino acids. The results for both parallel DE algorithms using both 2D and 3D models were compared with other works in the literature. The DE algorithm achieved excellent results. Overall results encourage further research towards the use of knowledge-based operators to improve further the performance of DE.

关键词： application program interfaces bioinformatics lattice theory message passing parallel algorithms proteins

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Table of contents

Table of contents

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ieee symposium on Computers & Informatics (ISCI)

Presents the table of contents/splash page of the proceedings record.

关键词： Computers computational intelligence Informatics Organizing Image processing bioinformatics computational biology

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Support vector-based fuzzy system for the prediction of mouse class I MHC peptide binding affinity

Support vector-based fuzzy system for the prediction of mous...

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ieee 13th International Conference on bioinformatics and Bioengineering (BIBE)

作者： Uslan, Volkan Seker, Huseyin De Montfort Univ Fac Technol Ctr Computat Intelligence Biohlth Informat Res Grp Leicester LE1 9BH Leics England

ISBN: (纸本)9781479931637

The performance of predictive models is crucial in order to accurately determine peptide binding affinity for major histocompatibility complex (MHC) alleles. Data sets extracted to model the relationship between the peptides and their binding affinities are often high-dimensional, complex and non-linear, which require highly sophisticated computational predictive models. Support Vector Machine (SVM)-based predictive methods have been used for such problems and have been shown to deal with such high dimensional data, however failed to take into account of uncertainty that naturally exists in this type of data. In order to address to the uncertainty issue, Fuzzy System (FS) has generally been utilised in various applications. Therefore, a hybrid method that combines FS and SVM is proposed in this study for the prediction of binding affinity of peptides in mouse class I MHC alleles. The hybrid system is successfully applied to two benchmark data sets of class I MHC peptides, each of which contains over 5000 peptide features. The assessments yield as much as 17% improvement over the previous studies that also include SVM-based experiments. The results also suggest positive impact of the concept of fuzziness on SVM-based predictive methods when combined and that the hybrid model can be generalised for similar non-linear system modelling problems.

关键词： biology computing cellular biophysics fuzzy set theory fuzzy systems genetics proteins support vector machines

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Technical program overview

Technical program overview

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ieee symposium on Computers & Informatics (ISCI)

Provides a schedule of conference events and a listing of which papers were presented in each session.

关键词： Computers computational intelligence Informatics Image processing bioinformatics computational biology Communications technology

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Critical Junction: Nonlinear Dynamics, Swarm intelligence and Cancer Research

Critical Junction: Nonlinear Dynamics, Swarm Intelligence an...

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ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Simon Rosenfeld National Cancer Institute Division Of Cancer Prevention

ISBN: (纸本)9781467358743

Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Despite astoundingly wide repertoire of observed forms, there are comparatively simple rules governing evolution of large systems towards self-organization, in general, and towards swarm intelligence, in particular. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) allowing for unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, to social networks, and to many other systems. The GCT provides a conceptual basis for understanding the emergent phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. A growing body of evidence confirms the premise that disruption of quorum sensing, an important aspect of swarm intelligence, plays a key role in carcinogenesis. Other aspects of swarm intelligence, such as collective memory, adaptivity (a form of learning from experience) and ability for self-repair are the key for understanding biological robustness and acquired chemoresistance. Yet another aspects of swarm intelligence, such as division of labor and competitive differentiation, may be helpful in understanding of cancer compartmentalization and tumor heterogene

关键词： Global Consensus Theorem Swarm intelligence Biomolecular Networks Carcinogenesis

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How to discriminate between potentially novel and considered biomarkers within molecular signature?

How to discriminate between potentially novel and considered...

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ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Ghim Siong OW Piroon JENJAROENPUN Jean Paul THIERY Vladimir A. KUZNETSOV Dept of Genome and Gene Expression Data Analysis Bioinformatics Institute Cancer Genetics and Therapeutics Institute of Molecular and Cell Biology

ISBN: (纸本)9781467358743

The lack of consensus among reported molecular (gene, protein, regulatory marker) signatures (MSs) in the literature is often an initial concern for researchers and subsequently it discourages larger scale prospective studies, prevent the translation of such knowledge into a practical clinical setting and ultimately hindering the progress of the field of biomarker-based disease classification, prognosis and prediction. Understanding the high level of clinical and biological heterogeneity in patients' cohort distribution, (e.g. by diseases subtypes and stages, age, treatment methods etc), limitations and misbalances in the number of samples, and uncertainty in the dimensionality of potential biomarker space, are critical for getting the signature consensus and identification of novel potential biomarkers. Differences in use of technological platforms, as well as variations in experimental protocols in different studies are also often contributing factors in the lack of strong consensus among signatures. In view of these differences, it would be inappropriate to compare MSs in entirety. Here, we investigate each variable in the signature of interest, and attempt to generate computationally "a null frequency distribution" of the expected number of co-occurrences in other MSs, i.e. other published MSs, and identify both novel and common biomarker within the given MS. We demonstrated an application of proposed model to identification of clinically essential genes of our somatically mutated genes in breast cancer.

关键词： Biomarker prediction Common biomarkers Molecular signature Biomarker frequency distribution Biomarkers consensus test Biomarker space Comparison of signatures Genome-wide Frequency distribution

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Integrated Analysis of Gene Expression and Genome-wide DNA Methylation for Tumor Prediction: An Association Rule Mining-based Approach

Integrated Analysis of Gene Expression and Genome-wide DNA M...

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ieee symposium on computational intelligence in bioinformatics and computational biology

作者： Saurav Mallik Anirban Mukhopadhyay Ujjwal Maulik Sanghamitra Bandyopadhyay Machine Intelligence Unit Indian Statistical Institute Department of Computer Science and Engineering University of Kalyani Department of Computer Science and Engineering Jadavpur University

ISBN: (纸本)9781467358743

Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.

关键词： Gene expression DNA methylation Statistical tests Association rule mining Genetic Algorithm (GA) based rank aggregation Rule-interestingness measures

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Advances in bioinformatics and computational biology - 8th Brazilian symposium on bioinformatics, BSB 2013, Proceedings

Advances in Bioinformatics and Computational Biology - 8th B...

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8th Brazilian symposium on bioinformatics, BSB 2013

ISBN: (纸本)9783319026237

The proceedings contain 19 papers. The topics discussed include: the trouble with long-range base pairs in RNA folding;roles of RORα on transcriptional expressions in the mammalian circadian regulatory system;HybHap: a fast and accurate hybrid approach for haplotype inference on large datasets;restricted DCJ-indel model revisited;MSA-GPU: exact multiple sequence alignment using GPU;MultiSETTER - multiple RNA structure similarity algorithm;assessing the accuracy of the SIRAH force field to model DNA at coarse grain level;how to multiply dynamic programming algorithms;influence of scaffold stability and electrostatics on Top7-based engineered helical HIV-1 epitopes;random forest and gene networks for association of SNPs to Alzheimer's disease;multilayer cluster heat map visualizing biological tensor data;and inference of genetic regulatory networks using an estimation of distribution algorithm.

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Optimizing Burrows-Wheeler Transform-Based Sequence Alignment on Multicore Architectures 13

Optimizing Burrows-Wheeler Transform-Based Sequence Alignmen...

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13th ieee/ACM International symposium on Cluster, Cloud, and Grid Computing (CCGrid)

作者： Zhang, Jing Lin, Heshan Balaji, Pavan Feng, Wu-chun Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9780769549965;9781467364652

computational biology sequence alignment tools using the Burrows-Wheeler Transform (BWT) are widely used in next-generation sequencing (NGS) analysis. However, despite extensive optimization efforts, the performance of these tools still cannot keep up with the explosive growth of sequencing data. Through an in-depth performance analysis of BWA, a popular BWT-based aligner on multicore architectures, we demonstrate that such tools are limited by memory bandwidth due to their irregular memory access patterns. We then propose a locality-aware implementation of BWA that aims at optimizing its performance by better exploiting the caching mechanisms of modern multicore processors. Experimental results show that our improved BWA implementation can reduce last-level cache (LLC) misses by 30% and translation lookaside buffer (TLB) misses by 20%, resulting in up to 2.6-fold speedup over the original BWA implementation.

关键词： short-read mapping Burrows-Wheeler transform FM-index bioinformatics data locality multicore

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Mining Quasi-Bicliques from HIV-1-Human Protein Interaction Network: A Multiobjective Biclustering Approach

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第2期10卷 423-435页

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Bhattacharyya, Malay Kaderali, Lars Brors, Benedikt Bandyopadhyay, Sanghamitra Eils, Roland Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Tech Univ Dresden Sch Med Inst Med Informat & Biometry D-01309 Dresden Germany Heidelberg Univ Dept Theoret Bioinformat German Canc Res Ctr D-69120 Heidelberg Germany Heidelberg Univ Bioquant D-69120 Heidelberg Germany Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs.

关键词： Protein-protein interaction HIV-1 biclustering quasi-biclique multiobjective optimization

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