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ieee conference on computational intelligence in bioinformatics and computational biology

作者： Pinoli, Pietro Chicco, Davide Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza L Da Vinci 32 I-20133 Milan Italy Univ Calif Irvine Inst Genom & Bioinformat Informat & Comp Sci Irvine CA USA

ISBN: (纸本)9781479945351

Gene function annotations are key elements in biology and bioinformatics. A typical annotation is the association between a gene and a feature term that describes a functional feature of the gene by using a controlled vocabulary term (e.g. a Gene Ontology (GO) feature term). Unfortunately, available annotations contain errors and biologically validated ones are incomplete by definition, since new knowledge is continuously discovered. Thus, computational algorithms which are able to provide ranked lists of predicted new gene annotations are an excellent contribution to the bioinformatics research. Here, we propose two variants of the known Latent Dirichlet Allocation (LDA) algorithm applied to the prediction of gene annotations. LDA is a very efficient machine learning method built on a set of multinomial probability distributions over a set of topics, given a document (a gene, in our case), and on a set of multinomial probability distributions over a set of words (feature terms, in our case), given a topic. In topic modeling, a topic can be considered as a latent meta-category of words, and a document as a mixture of topics. Our two LDA variants use the collapsed Gibbs Sampling method during the training phase, with two distinct initialization approaches to adapt the LDA mathematical model to the biomolecular annotation scenario. Using six outdated datasets of GO annotations of human and brown rat genes, we compared the annotations predicted by our methods to the ones given by the truncated Singular Value Decomposition (tSVD) method previously developed;then, we validated them by using the annotations available in an updated version of the same datasets. Obtained results show the efficiency of our new proposed algorithms.

关键词： ANNOTATIONS Gene function Gene Annotation Probability distribution laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Gibbs Sampling Genes bioinformatics Gene Ontology vif Genes

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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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Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Minimal-redundancy-maximal-relevance feature selection using...

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10th Annual ieee Symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Yang, Junshan Zhu, Zexuan He, Shan Ji, Zhen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen City Key Lab Embedded Syst Design Shenzhen 518060 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781467358750

Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal-relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.

关键词： biology computing learning (artificial intelligence) pattern classification statistical analysis

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9th International conference on Practical Applications of computational biology and bioinformatics 1

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丛书名： Advances in Intelligent Systems and Computing 375

2015年

作者： Ross Overbeek, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz (eds.)

ISBN: (数字)9783319197753;9783319197760

ISBN: (纸本)9783319197753;9783319197760

This proceedings presents recent practical applications of computational biology and bioinformatics. It contains the proceedings of the 9th International conference on Practical Applications of computational biology & bioinformatics held at University of Salamanca, Spain, at June 3rd-5th, 2015. The International conference on Practical Applications of computational biology & bioinformatics (PACBB) is an annual international meeting dedicated to emerging and challenging applied research in bioinformatics and computational biology. Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of bioinformatics and computational biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial intelligence. Clearly, biology is more and more a science of information requiring tools from the computational sciences.

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A machine learning approach to resolving incongruence in molecular phylogenies and visualization analysis

A machine learning approach to resolving incongruence in mol...

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2005 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '05

作者： Xiaoxu, Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI 48197 United States

ISBN: (纸本)0780393872

The incongruence between gene trees and species trees is one of the most pervasive challenges in molecular phylogenetics. In this work, a machine learning approach is proposed to overcome this problem. In the machine learning approach, the gene data set is clustered by a self-organizing map (SOM). Then a phylogenetically informative core gene set is created by combining the maximum entropy gene from each cluster to conduct phylogenetic analysis. Using the same data set, this approach performs better than the previous random gene concatenation method. The SOM based information visualization is also employed to compare the species patterns in the phylogenetic tree constructions. © 2005 ieee.

关键词： Learning systems

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Guest Editorial for Special Section on the 10th International conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2016年第1期13卷 1-3页

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

This special section includes a selection of eight papers presented at the 10th International conference on Intelligent Computing (ICIC) held in Taiyuan, China, on August 3–6, 2014.

关键词： Special issues and sections Meetings bioinformatics computational biology

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bioinformatics Sister Courses An Interdisciplinary Collaborative Learning Framework To Teach bioinformatics

Bioinformatics Sister Courses An Interdisciplinary Collabora...

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International conference on computational Science and computational intelligence (CSIC)

作者： Harris, Elena Y. Keller, David Stachura, David Wolfe, Gordon Calif State Univ Dept Comp Sci Chico CA 95929 USA Calif State Univ Dept Biol Chico CA USA

ISBN: (纸本)9781509055104

Technological advances in DNA sequencing due to Next Generation Sequencing (NGS) technology revolutionized research in many areas including medicine. bioinformatics as a science has developed to address computational challenges related to the analyses of large amounts of data generated by NGS technology. Consequently, educators faced challenges in developing effective methods to teach bioinformatics. This paper presents a pilot study, in which an interdisciplinary collaborative learning approach combined two sister courses in bioinformatics for computer science and biology students concurrently. Projectbased research that included the analysis of NGS data was incorporated in these courses and resulted in a journal article. The present paper will contribute to the ongoing dialog between the educators about the most effective ways of teaching bioinformatics.

关键词： bioinformatics undergraduate research experiences collaborative learning multi-disciplinary initiatives team projects

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Early Lung Cancer Detection using Nucleus Segementation based Features

Early Lung Cancer Detection using Nucleus Segementation base...

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10th Annual ieee Symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Kancherla, Kesav Mukkamala, Srinivas New Mexico Inst Min & Technol ICASA CAaNES Socorro NM 87801 USA

ISBN: (纸本)9781467358750

In this study we propose an early lung cancer detection methodology using nucleus based features. First the sputum samples from patients are labeled with Tetrakis Carboxy Phenyl Porphine (TCPP) and fluorescent images of these samples are taken. TCPP is a porphyrin that is able to assist in labeling lung cancer cells by increasing numbers of low density lipoproteins coating on the surface of cancer. We study the performance of well know machine learning techniques in the context of lung cancer detection on Biomoda dataset. We obtained an accuracy of 81% using 71 features related to shape, intensity and color in our previous work. By adding the nucleus segmented features we improved the accuracy to 87%. Nucleus segmentation is performed by using Seeded region growing segmentation method. Our results demonstrate the potential of nucleus segmented features for detecting lung cancer.

关键词： Lung Cancer detection bioinformatics Machine Learning Seeded Region Growing segmentation

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A systematic computational approach for transcription factor target gene prediction

A systematic computational approach for transcription factor...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： He, Ji Dai, Xinbin Zhao, Xuechun Samuel Roberts Noble Fdn Inc Div Plant Biol Bioinformat Grp 2510 Sam Noble Pkwy Ardmore OK 73401 USA

ISBN: (纸本)1424406234

computational prediction of transcription factor's binding sites and regulatory target genes has great value to the biological studies of cellular process. Existing practices either look into first-hand gene expression data which could be costly for large scale analysis, or apply statistical or heuristic learning methods to discover potential binding sites which have limited accuracy due to the complexity of the data. Based on well-studied information retrieval theories, this paper proposes a novel systematic approach for transcription factor target gene prediction. The key of the approach is to model the prediction problem as a classification task by representing the features of the sequential data into vector data points in a higher-order domain. The proposed approach has produced satisfactory results in our controlled experiment on Auxin Response Factor (ARF) target gene prediction in Arabidopsis.

关键词： Transcription factors

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Guest Editorial for the 14th Asia Pacific bioinformatics conference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 396-397页

作者： Tang, Jijun Tian, Lu Chen, Yi-Ping Phoebe Univ Southern Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Stanford Univ Dept Biomed Data Sci Stanford CA 94305 USA La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Bundoora Vic 3086 Australia

The eight papers in this special section were presented at the 14th Asia Pacific bioinformatics conference (APBC2016), which was held in San Francisco, USA, 11-13 January 2016.

关键词： Special Issues And Sections bioinformatics Meetings

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