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6th International conference on Practical Applications of computational biology & bioinformatics 2012

丛书名： Advances in Intelligent and Soft Computing

2012年

作者： Miguel P. Rocha Nicholas Luscombe Florentino Fdez-Riverola Juan M. Corchado Rodríguez

ISBN: (数字)9783642288395

ISBN: (纸本)9783642288388

The growth in the bioinformatics and computational biology fields over the last few years has been remarkable and the trend is to increase its pace. In fact, the need for computational techniques that can efficiently handle the huge amounts of data produced by the new experimental techniques in biology is still increasing driven by new advances in Next Generation Sequencing, several types of the so called omics data and image acquisition, just to name a few. The analysis of the datasets that produces and its integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial intelligence. Within this scenario of increasing data availability, Systems biology has also been emerging as an alternative to the reductionist view that dominated biological research in the last decades. Indeed, biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of bioinformatics scientists. PACBB12 hopes to contribute to this effort promoting this fruitful interaction. PACBB'12 technical program included 32 papers from a submission pool of 61 papers spanning many different sub-fields in bioinformatics and computational biology. Therefore, the conference will certainly have promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists). The scientific content will certainly be challenging and will promote the improvement of the work that is being developed by each of the pa

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Guest Editorial for the 15th Asia Pacific bioinformatics conference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第5期15卷 1513-1514页

作者： Wang, Lusheng Li, Shuai Cheng Chen, Yi-Ping Phoebe City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Natl Univ Singapore Database Res Grp Singapore Singapore City Univ Hong Kong Hong Kong Hong Kong Peoples R China La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic Australia ARC Ctr Excellence Bioinformat St Lucia Qld Australia

No abstract available.

关键词： Special issues and sections Meetings bioinformatics

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A wrapper feature selection method based on simulated annealing algorithm for prostate protein mass spectrometry data

A wrapper feature selection method based on simulated anneal...

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2008 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '08

作者： Li, Yifeng Liu, Yihui Institute of Intelligence Information Processing School of Information Science and Technology Shandong Institute of Light Industry Jinan Shandong 250353 China

ISBN: (纸本)9781424417780

Protein mass spectrometry is an integration of mass spectrometry and biological chip techniques, and it shows great potential for exploration of biomarkers and diagnosis of diseases. But the curse of dimensionality inherently from mass spectrometry data makes the dimensionality reduction a necessary phase of proteomic pattern recognition before classification. This paper presents a simulated annealing algorithm to select discriminant feature subsets. Experiments indicate that this wrapper feature selection method performs well and outperforms the other reported methods. © 2008 ieee.

关键词： Simulated annealing

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A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for computational and Systems biology Applications 11th

A Fine-Grained CUDA Implementation of the Multi-objective Ev...

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11th International Meeting on computational intelligence Methods for bioinformatics and Biostatistics (CIBB)

作者： D'Agostino, Daniele Pasquale, Giulia Merelli, Ivan Natl Res Council Italy Inst Appl Math & Informat Technol I-16149 Genoa Italy Natl Res Council Italy Inst Biomed Technol I-20090 Segrate Mi Italy

ISBN: (纸本)9783319244624;9783319244617

Many computational and systems biology challenges, in particular those related to big data analysis, can be formulated as optimization problems and therefore can be addressed using heuristics. Beside the typical optimization problems, formulated with respect to a single target, the possibility of optimizing multiple objectives (MO) is rapidly becoming more appealing. In this context, MO Evolutionary Algorithms (MOEAs) are one of the most widely used classes of methods to solve MO optimization problems. However, these methods can be particularly demanding from the computational point of view and, therefore, effective parallel implementations are needed. This fact, together with the wide diffusion of powerful and low-cost general-purpose Graphics Processing Units, promoted the development of software tools that focus on the parallelization of one or more computational phases among the steps characterizing MOEAs. In this paper we present a fine-grained parallelization of the Fast Non-dominating Sorting Genetic Algorithm (NSGA-II) for the CUDA architecture. In particular, we will discuss how this solution can be exploited to solve multi-objective optimization task in the field of computational and systems biology.

关键词： Graphics Processing Units CUDA-accelerated architecture Multi-objective optimization computational and Systems biology

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Enhanced Probabilistic Latent Semantic Analysis with Weighting Schemes to Predict Genomic Annotations

Enhanced Probabilistic Latent Semantic Analysis with Weighti...

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

作者： Pinoli, Pietro Chicco, Davide Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781479931637

Genomic annotations with functional controlled terms, such as the Gene Ontology (GO) ones, are paramount in modern biology. Yet, they are known to be incomplete, since the current biological knowledge is far to be definitive. In this scenario, computational methods that are able to support and quicken the curation of these annotations can be very useful. In a previous work, we discussed the benefits of using the Probabilistic Latent Semantic Analysis algorithm in order to predict novel GO annotations, compared to some Singular Value Decomposition (SVD) based approaches. In this paper, we propose a further enhancement of that method, which aims at weighting the available associations between genes and functional terms before using them as input to the predictive system. The tests that we performed on the annotations of human genes to GO functional terms showed the efficacy of our approach.

关键词： biology computing genomics ontologies (artificial intelligence)

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Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2013年第3期10卷 742-751页

作者： Sengupta, Debarka Pyne, Aroonalok Maulik, Ujjwal Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Jadavpur Univ Comp Sci & Engn Dept Kolkata 700032 W Bengal India

MicroRNAs are very recently discovered small noncoding RNAs, responsible for negative regulation of gene expression. Members of this endogenous family of small RNA molecules have been found implicated in many genetic disorders. Each microRNA targets tens to hundreds of genes. Experimental validation of target genes is a time- and cost-intensive procedure. Therefore, prediction of microRNA targets is a very important problem in computational biology. Though, dozens of target prediction algorithms have been reported in the past decade, they disagree significantly in terms of target gene ranking (based on predicted scores). Rank aggregation is often used to combine multiple target orderings suggested by different algorithms. This technique has been used in diverse fields including social choice theory, meta search in web, and most recently, in bioinformatics. Kemeny optimal aggregation (KOA) is considered the more profound objective for rank aggregation. The consensus ordering obtained through Kemeny optimal aggregation incurs minimum pairwise disagreement with the input orderings. Because of its computational intractability, heuristics are often formulated to obtain a near optimal consensus ranking. Unlike its real time use in meta search, there are a number of scenarios in bioinformatics (e.g., combining microRNA target rankings, combining disease-related gene rankings obtained froth microarray experiments) where evolutionary approaches can be afforded with the ambition of better optimization. We conjecture that an ideal consensus ordering should have its total disagreement shared, as equally as possible, with the input orderings. This is also important to refrain the evolutionary processes from getting stuck to local extremes. In the current work, we reformulate Kemeny optimal aggregation while introducing a trade-off between the total pairwise disagreement and its distribution: A simulated annealing-based implementation of the proposed objective has been found effe

关键词： Kemeny optimal aggregation rank aggregation microRNA target prediction simulated annealing optimization

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Application of Deep Learning Models to MicroRNA Transcription Start Site Identification 7

Application of Deep Learning Models to MicroRNA Transcriptio...

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ieee 7th International conference on bioinformatics and computational biology (ICBCB)

作者： Barham, Clayton Cha, Mingyu Li, Xiaoman Hu, Haiyan Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781728106410

MicroRNAs (miRNA) are similar to 22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.

关键词： bioinformatics transcriptional regulation transcription start sites neural networks deep learning

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10th International conference on Practical Applications of computational biology & bioinformatics 1st ed. 2016

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丛书名： Advances in Intelligent Systems and Computing

2016年

作者： Mohd Saberi Mohamad Miguel P. Rocha Florentino Fdez-Riverola Francisco J. Domínguez Mayo Juan F. De Paz

ISBN: (数字)9783319401263

ISBN: (纸本)9783319401256

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. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of bioinformatics scientists. PACBB16 hopes to contribute to this effort promoting this fruitful interaction. PACBB'16 technical program included 21 papers spanning many different sub-fields in bioinformatics and computational biology. Therefore, the conference will certainly promote the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists). The scientific content will certainly be challenging and will promote the improvement of the work being developed by each of the participants.

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Sorting Signed Permutations by Inverse Tandem Duplication Random Losses

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2177-2188页

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Univ Leipzig Fac Math & Comp Sci Swarm Intelligence & Complex Syst Grp Augustuspl 10 D-04109 Leipzig Germany Univ Lubeck Inst Theoret Comp Sci D-23562 Lubeck Germany

Gene order evolution of unichromosomal genomes, for example mitochondrial genomes, has been modelled mostly by four major types of genome rearrangements: inversions, transpositions, inverse transpositions, and tandem duplication random losses. Generalizing models that include all those rearrangements while admitting computational tractability are rare. In this paper, we study such a rearrangement model, namely the inverse tandem duplication random loss (iTDRL) model, where an iTDRL duplicates and inverts a continuous segment of a gene order followed by the random loss of one of the redundant copies of each gene. The iTDRL rearrangement has currently been proposed by several authors suggesting it to be a possible mechanisms of mitochondrial gene order evolution. We initiate the algorithmic study of this new model of genome rearrangement by proving that a shortest rearrangement scenario that transforms one given gene order into another given gene order can be obtained in quasilinear time. Furthermore, we show that the length of such a scenario, i.e., the minimum number of iTDRLs in the transformation, can be computed in linear time.

关键词： Sorting Genomics computational modeling Approximation algorithms Biological cells Evolution (biology) Biological system modeling Inverse tandem duplication random loss mitochondria genome rearrangements sorting problem distance problem

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Critical Junction: Nonlinear Dynamics, Swarm intelligence and Cancer Research

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

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

作者： Rosenfeld, Simon NCI Canc Prevent Div Bethesda MD 20892 USA

ISBN: (纸本)9781467358750

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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