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8th ieee symposium Series on computational intelligence (ieee SSCI)

作者： Mehra, Neha Tiwari, Aruna Ratnaparkhe, Milind B. Bharill, Neha Indian Inst Technol Indore Discipline Comp Sci & Engn Indore Madhya Pradesh India ICAR IISR Indore Indore Madhya Pradesh India Indian Inst Informat Technol Dharwad Dept Comp Sci & Engn Dharwad Karnataka India

ISBN: (纸本)9781538692769

bioinformatics has emerged as one of the most challenging research area which combines machine learning and biological techniques for analysis of biological sequence data. The protein sequence classification is an important task in the field of bioinformatics. The main aim is to process such data so that it can be made usable to be provided as input to the machine learning algorithm. In this paper, a feature extraction approach is used for converting protein sequences of cyclophilin superfamily into the feature vectors. The feature vectors are then fed as an input to three classifiers, i.e. SVM, K-NN, NB. The experimentation results are presented in the form of performance analysis of all three classifiers in terms of classification of protein sequences of cyclophilin superfamily.

关键词： bioinformatics Protein sequences Feature extraction Classification

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Guest Editorial for Special Section on the 12th International Conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第5期15卷 1433-1435页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai 201804 Peoples R China Polytech Bari Elect & Informat Engn Dept I-70126 Bari BA Italy Indian Inst Technol IIT Madras Dept Biotechnol Madras 600036 Tamil Nadu India

The eight papers included in this special section were presented at the 12th International Conference on Intelligent Computing (ICIC) held at Lanzhou, China, during August 2-5, 2016. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Meetings bioinformatics Machine learning Intelligent systems

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Data Driven Point Packing for Fast Clustering

Data Driven Point Packing for Fast Clustering

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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Stoodley, Matthew Ashlock, Daniel Graether, Steffen Univ Guelph Bioinformat Program Guelph ON Canada Univ Guelph Dept Math & Stat Guelph ON Canada Univ Guelph Dept Mol & Cellular Biol Guelph ON Canada

ISBN: (纸本)9781538613993

Modern data acquisition has forced the field of large data on the scientific community. This papers gives a rapid technique for clustering data. The technique is based on an off-line process for packing points chosen from a data space. Once the off-line process has been run, the clustering may be re-run on different data sets of the same type in linear time. The clustering takes the form of a Voronoi tiling of the data space with the tile centres being the elements of the point packing. The data items within each tile form the clusters. The evolutionary algorithm is an adaptation of one, based on the Conway crossover operator, that has been used to create error correcting codes over the Levenstein metric;the tile centres are a form of code, but over the Euclidean metric. The technique generalizes smoothly to other metric spaces and may be used on any type of data for which a distance metric can be devised. The data set used in this study captures information about codon usage bias in human genes. The clustering is validated by looking for GO term over-representation in the clusters, with significant results.

关键词： Amino acids Clustering algorithms RNA DNA Measurement bioinformatics Hypercubes

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Aggregation of Subclassifications: Methods, Tools and Experiments

Aggregation of Subclassifications: Methods, Tools and Experi...

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ieee symposium Series on computational intelligence (SSCI)

作者： Christoph Doell Christian Borgelt Chair for Bioinformatics and Information Mining University of Konstanz Konstanz Germany

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Aggregation methods have been studied extensively from a mathematical, theoretical point of view. In this work, however, we focus on a more practical aspect: subclassifications. Given class predictions for several sub-objects of a single instance, we systematically investigate the performance of different aggregation methods. To this end, we simulate data for various data distributions. Thus we ensure that we know the ground truth for the evaluation, which would be impossible for real world data. Our source code is publicly available and can be extended to explore other aggregation methods and other data distributions.

关键词： Exponential distribution Pattern classification Data aggregation

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Towards Extracting Supporting Information About Predicted Protein-Protein Interactions

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第4期15卷 1239-1246页

作者： Roth, Adam Subramanian, Sandeep Ganapathiraju, Madhavi K. Univ Pittsburgh Dept Biomed Informat 5607 Baum BlvdSuite 501 Pittsburgh PA 15206 USA Carnegie Mellon Univ Langauge Technol Inst 5000 Forbes Ave Pittsburgh PA 15213 USA

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.

关键词： Artificial intelligence computational biology/methods data mining/methods semantics proteins/metabolism natural language processing

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A Simplified Description of Child Tables for Sequence Similarity Search

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第6期15卷 2067-2073页

作者： Frith, Martin C. Shrestha, Anish M. S. Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 3058567 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 1138654 Japan Waseda Univ CBBD OIL AIST Tokyo 1698050 Japan

Finding related nucleotide or protein sequences is a fundamental. diverse, and incompletely-solved problem in bioinformatics. It is often tackled by seed-and-extend methods, which first find "seed" matches of diverse types, such as spaced seeds, subset seeds, or minimizers. Seeds are usually found using an index of the reference sequence(s), which stores seed positions in a suffix array or related data structure. A child table is a fundamental way to achieve fast lookup in an index, but previous descriptions have been overly complex. This paper aims to provide a more accessible description of child tables, and demonstrate their generality: they apply equally to all the above-mentioned seed types and more. We also show that child tables can be used without LCP (longest common prefix) tables, reducing the memory requirement.

关键词： biology and genetics indexing methods data structures arrays trees

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2017 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2017

2017 IEEE Conference on Computational Intelligence in Bioinf...

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2017 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2017

ISBN: (纸本)9781467389884

The proceedings contain 48 papers. The topics discussed include: leveraging data mining techniques to understand drivers of obesity;applying brain emotional learning based fuzzy inference system for EEG signal classification between schizophrenics and control participant;inference of genetic networks from time-series of gene expression levels using random forests;biclustering of gene expression microarray data using dynamic deme parallelized genetic algorithm(DdPGA);a multivariate feature selection framework for high dimensional biomedical data classification;a novel representation for Boolean networks designed to enhance heritability and scalability;inferring bistable LAC operon Boolean regulatory networks using evolutionary computation;hybridization and ring optimization for larger sets of embeddable biomarkers;modeling protein structural transitions as a multi-objective optimization problem;binning metagenomic reads with probabilistic sequence signatures based on spaced seeds;topological, functional, and structural analyses of protein-protein interaction networks of breast cancer lung and brain metastases;a novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions;evaluation of support vector machines and random forest classifiers in a real-time fetal monitoring system based on cardiotocography data;and reboot strategies in particle swarm optimization and their impact on parameter estimation of biochemical systems.

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computational Modeling of Psychological Resilience Trajectories During Breast Cancer Treatment

Computational Modeling of Psychological Resilience Trajector...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Georgios C. Manikis Konstantina Kourou Paula Poikonen-Saksela Haridimos Kondylakis Evangelos Karademas Kostas Marias Dimitrios G. Katehakis Lefteris Koumakis Angelina Kouroubali Ruth Pat-Horenczyk Dimitrios I. Fotiadis Manolis Tsiknakis Panagiotis Simos FORTH-ICS Computational Biomedicine Laboratory Heraklion Greece Institute of Molecular Biology and Biotechnology FORTH-IMBB Ioannina Greece Comprehensive Cancer Center Helsinki University Hospital Helsinki Finland Computational Biomedicine Laboratory FORTH-ICS & University of Crete Heraklion Greece Computational Biomedicine Laboratory FORTH-ICS & Hellenic Mediterranean University Heraklion Greece School of Social Work Hebrew University Jerusalem Israel

ISBN: (纸本)9781728146188

Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.

关键词： Resilience Trajectory Psychology Breast cancer Predictive models Adaptation models

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Heuristics for the Specific Substring Problem with Hamming Distance

Heuristics for the Specific Substring Problem with Hamming D...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Lucas B. Rocha Said Sadique Adi Marco A. Stefanes Eloi Araujo Faculdade de Computação Universidade Federal de Mato Grosso de Sul Campo Grande MS Brazil Universidade Federal de Mato Grosso de Sul Campo Grande Brazil

ISBN: (纸本)9781728146188

An important problem in computational biology is to determine genetic markers, substrings of a set of sequences that do not occur on sequences of other sets. Applications for this problem include finding small specific regions for primer design and to find specific organisms or sequences in metagenomes. Genetic markers can be addressed by the Specific Substring Problem - SSP which consists of finding all minimal substrings in a given set of sequences with at least k differences among all the substrings in another sequence set. Since this problem spend quadratic time when Hamming distance is considered and we have, in general, a large volume of data to be processed, this solution becomes impractical. With this in mind, the main focus of this work is to propose and investigate the use of heuristic and parallel approaches for the SSP whose effectiveness were verified with artificial and real data experiments.

关键词： Hamming distance Conferences Genetics Organisms bioinformatics computational biology Biomedical engineering

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Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 673-687页

作者： Bandyopadhyay, Sanghamitra Mallik, Saurav Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers (SCMs) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., CoMEx) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support (WS) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential SCITs. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

关键词： SCM CoMEx (maximal biclustered) frequent closed homogeneous genesets sample-based weighted support measure gene expression and methylation data

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