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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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Latent Dirichlet Allocation based on Gibbs Sampling for Gene Function Prediction

Latent Dirichlet Allocation based on Gibbs Sampling for Gene...

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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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Application of computational intelligence to biology 1

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丛书名： SpringerBriefs in Applied Sciences and Technology

2016年

作者： Ravi Bhramaramba, Akula Chandra Sekhar (eds.)

ISBN: (数字)9789811003912

ISBN: (纸本)9789811003905;9789811003912

This book is a contribution of translational and allied research to the proceedings of the International conference on computational intelligence and Soft Computing. It explains how various computational intelligence techniques can be applied to investigate various biological problems. It is a good read for Research Scholars, Engineers, Medical Doctors and bioinformatics researchers.

关键词： computational intelligence computational and Systems biology Diseases Proteins Biomedical Engineering and Bioengineering Health Informatics

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bioinformatics: Implementation of a proposed upgraded smith-waterman algorithm for local alignment

Bioinformatics: Implementation of a proposed upgraded smith-...

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2009 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2009

作者： Sadi, Md. Shihabuddin Mohammad Sami, AbuZ. Uddin Ahmed, Imtiaz Ruhunnabi, Abdullahil Baki Md. Das, Nirmalendu University of Science and Technology

ISBN: (纸本)9781424427567

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 a database in order to find. © 2009 ieee.

关键词： bioinformatics

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Guest Editorial: Introduction to the Special Issue on bioinformatics Research and Applications

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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ieee TRANSACTIONS ON NANOBIOSCIENCE 2016年第2期15卷 74-76页

作者： He, Jing Li, Yaohang Zelikovsk, Alexander Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Old Dominion Univ Comp Sci Norfolk VA 23529 USA Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

This Special Issue includes a selection of papers presented at the 11th International Symposium on bioinformatics Research and Application (ISBRA), which was held at Old Dominion University in Norfolk, VA, USA, on May 7-10, 2015. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2015, 98 papers were submitted in response to the call for papers, out of which 12 papers were invited to submit extended versions of their conference abstracts to this Special Issue. Selected papers illustrate the variety of applications that computational methods find in the field of nanobioscience, ranging from protein classification to de novo sequencing. Furthermore, selected papers convincingly demonstrate the central role played by computational methods in contemporary nanobioscience research - a role that is bound only to increase in the future. The Guest Editors then briefly describe each of 12 accepted papers.

关键词： Special issues and sections Meetings bioinformatics

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A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem

A multimodal multi-objective optimisation approach to deal w...

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

作者： Rivera, Cesar Inostroza-Ponta, Mario Villalobos-Cid, Manuel Univ Santiago Chile Dept Ingn Informat Santiago Chile

ISBN: (纸本)9781728194684

The phylogenetic inference strategies aim to propose hypotheses to explain the evolutionary relationships for different organisms. These resultant evolutionary histories are often represented as phylogenetic trees. In computer science, the phylogenetic inference has been treated as an optimisation problem. The literature has proposed different criteria to select the optimal tree between the possible topologies. In order to reduce the bias associated to the dependency on the selected criterion, different multi-objective optimisation strategies have been proposed during the last decade. These strategies search by solutions using operators and metrics based on the objective space. However, a recent work concluded that the topological features of the trees (decision space) and the objective space in the multi-objective phylogenetic inference context are not related, becoming phylogeny in a multimodal problem. It means that the current multi-objective strategies could discard solutions from different regions of the decision space, limiting the searching process and the resultant topologies. In this work, we propose a new version of the Memetic algorithm based on an NSGA-II scheme for phylogenetic inference, which include a multimodal operator that considers the diversity of the topologies of the trees based on the decision space to rank the solutions. The inclusion of this operator improved the diversity of solutions according to the decision and the objective space, increasing the hypervolume metric compared to the base version of this memetic algorithm.

关键词： multi-objective multimodal optimisation bioinformatics phylogenetic inference

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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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Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence

Inference of gene regulatory networks from time course gene ...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： Ressom, H. W. Zhang, Y. Xuan, J. Wang, Y. Clarke, R. Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20057 USA Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 24061 USA

ISBN: (纸本)1424406234

We present a novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of an RNN, while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN whose response mimics gene expression data generated by time course DNA microarray experiments. We observed promising results in applying the proposed hybrid SI-RNN algorithm to infer networks of interaction from simulated and real-world gene expression data.

关键词： Recurrent neural networks

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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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