检索结果-内蒙古大学图书馆

bioinformatics 2015 - 6th international conference on bioinformatics models, methods and algorithms, Proceedings;Part of 8th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015

BIOINFORMATICS 2015 - 6th International Conference on Bioinf...

引用

6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

ISBN: (纸本)9789897580703

the proceedings contain 34 papers. the topics discussed include: how to cross the border from R to D? - the example of conception of new medical devices;the bidirectional route of research and innovation - insights from the stressed brain;modeling genetical data with forests of latent trees for applications in association genetics at a large scale - which clustering method should be chosen?;essential proteins and functional modules in the host-pathogen interactions from innate to adaptive immunity - c. albicans-zebrafish infection model;identifying aging genes in the aging mouse hypothalamus using gateway node analysis of correlation networks;machine reading of biological texts - bacteria-biotope extraction;wave equation model of soft tissue for a virtual reality laparoscopy training system - a validation study;a nonlinear mixture model based unsupervised variable selection in genomics and proteomics;and approximate analysis of homeostasis of gene networks by linear temporal logic using network motifs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection 18th

Synergies of Deep and Classical Exploratory Landscape Feat...

引用

18th international conference on Learning and Intelligent Optimization, LION 2024

作者： Seiler, Moritz Škvorc, Urban Doerr, Carola Trautmann, Heike Machine Learning and Optimisation University of Paderborn Paderborn Germany Sorbonne Université CNRS ParisLIP6 France DMB Group University of Twente Enschede Netherlands

ISBN: (纸本)9783031756221

Per-instance automated algorithm selection (AAS) aims at leveraging the complementarity of optimization algorithms with respect to different problem types. State-of-the-art AAS methods for numerical black-box optimization rely on supervised learning techniques that are supported by exploratory landscape analysis (ELA) feature sets. Recent works question the generalization ability of popular AAS approaches, which motivated the design of alternative feature sets. In this work, we take a closer look at the recently proposed set of Deep ELA features and investigate the ways in which Deep ELA complements the classical ELA feature sets. To this end, we first study the correlation between the two feature collections, both through pairwise classification and through regression models. the complementarity observed in these analyses is confirmed by an AAS study, where models combining deep and classical features outperform those that are restricted to selecting from only of the two collections. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Towards a Parallel Code for Cellular Behavior in Vitro Prediction 4th

Towards a Parallel Code for Cellular Behavior in Vitro Pr...

引用

4th international conference on Numerical Computations: theory and algorithms, NUMTA 2023

作者： De Luca, Pasquale Galletti, Ardelio Marcellino, Livia Department of Mathematics University Carlos III of Madrid Leganes Spain International PhD Programme / UNESCO Chair "Environment Resources and Sustainable Development" Department of Science and Technology Parthenope University of Naples Centro Direzionale Isola C4 Naples Italy Science and Technologies Department Parthenope University of Naples Naples Italy

ISBN: (纸本)9783031812460

In recent years, there has been an increasing interest in developing in vitro models that predict the behavior of cells in living organisms. Mathematical models based on differential equations, and related numerical algorithms, have been provided to this aim. In this work, we present first experiences in designing parallel strategies for accelerating an algorithm for behavior prediction based on the Cellular Potts Model (CPM). In particular, we exploit the computational power of Graphic Process Units in CUDA environment to address main low-level kernels involved. Tests and experiments complete the paper. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Potts model

来源：评论

学校读者我要写书评

暂无评论

algorithms for regularized linear discriminant analysis 6

Algorithms for regularized linear discriminant analysis

引用

6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Kalina, Jan Tebbens, Jurjen Duintjer Dept. of Medical Informatics and Biostatistics Institute of Computer Science AS CR Prague Czech Republic Dept. of Computational Methods Institute of Computer Science AS CR Prague Czech Republic Dept. of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Králové Charles University in Prague Hradec Králové Czech Republic

ISBN: (纸本)9789897580703

this paper is focused on regularized versions of classification analysis and their computation for high-dimensional data. A variety of regularized classification methods has been proposed and we critically discuss their computational aspects. We formulate several new algorithms for regularized linear discriminant analysis, which exploits a regularized covariance matrix estimator towards a regular target matrix. Numerical linear algebra considerations are used to propose tailor-made algorithms for specific choices of the target matrix. Further, we arrive at proposing a new classification method based on L2-regularization of group means and the pooled covariance matrix and accompany it by an efficient algorithm for its computation.

关键词： Covariance matrix

来源：评论

学校读者我要写书评

暂无评论

Towards a unified Named Entity Recognition system disease mention identification 6

Towards a unified Named Entity Recognition system disease me...

引用

6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Munkhdalai, Tsendsuren Li, Meijing Batsuren, Khuyagbaatar Ryu, Keun Ho Database/Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju Korea Republic of

ISBN: (纸本)9789897580703

Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biomedical text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. In this study, we take a step towards a unified NER system in biomedical, chemical and medical domain. We evaluate word representation features automatically learnt by a large unlabeled corpus for disease NER. the word representation features include brown cluster labels and Word Vector Classes (WVC) built by applying k-means clustering to continuous valued word vectors of Neural Language Model (NLM). the experimental evaluation using Arizona Disease Corpus (AZDC) showed that these word representation features boost system performance significantly as a manually tuned domain dictionary does. BANNER-CHEMDNER, a chemical and biomedical NER system has been extended with a disease mention recognition model that achieves a 77.84% F-measure on AZDC when evaluating with 10-fold cross validation method. BANNER-CHEMDNER is freely available at: https://***/tsendeemts/banner-chemdner.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

PERFECTOS-APE: Predicting regulatory functional effect of SNPs by approximate P-value estimation 6

PERFECTOS-APE: Predicting regulatory functional effect of SN...

引用

6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Vorontsov, Ilya E. Kulakovskiy, Ivan V. Khimulya, Grigory Nikolaeva, Daria D. Makeev, Vsevolod J. Department of Computational Systems Biology Vavilov Institute of General Genetics Gubkina Str. 3 Moscow Russia Laboratory of Bioinformatics and Systems Biology Engelhardt Institute of Molecular Biology Vavilova Str. 32 Moscow Russia Faculty of Bioengineering and Bioinformatics Lomonosov Moscow State University Moscow Russia Department of Medical and Biological Physics Moscow Institute of Physics and Technology Moscow Region Russia

ISBN: (纸本)9789897580703

Single nucleotide polymorphisms (SNPs) and variants (SNVs) are often found in regulatory regions of human genome. Nucleotide substitutions in promoter and enhancer regions may affect transcription factor (TF) binding and alter gene expression regulation. Nowadays binding patterns are known for hundreds of human TFs. thus one can assess possible functional effects of allele variations or mutations in TF binding sites using sequence analysis. We present PERFECTOS-APE, the software to PrEdict Regulatory Functional Effect of SNPs by Approximate P-value Estimation. Using a predefined collection of position weight matrices (PWMs) representing TF binding patterns, PERFECTOS-APE identifies transcription factors whose binding sites can be significantly affected by given nucleotide substitutions. PERFECTOS-APE supports both classic PWMs under the position independency assumption, and dinucleotide PWMs accounting for the dinucleotide composition and correlations between nucleotides in adjacent positions within binding sites. PERFECTOS-APE uses dynamic programming to calculate PWM score distribution and convert the scores to P-values with an optional binary search mode using a precomputed P-value list to speed-up the computations. Software is written in Java and is freely available as standalone program and online tool: http://***/perfectosape/. We have tested our algorithm on several disease associated SNVs as well as on a set of cancer somatic mutations occurring in intronic regions of the human genome.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

A study on the application of topic models to motif finding algorithms

引用

BMC bioinformatics 2016年第SUPPL 19期17卷 129-138页

作者： Gutierrez, Josep Basha Nakai, Kenta Univ Tokyo Grad Sch Frontier Sci Dept Computat Biol & Med Sci Chiba 2778561 Japan Univ Tokyo Inst Med Sci Human Genome Ctr Minato Ku 4-6-1 Shirokane Dai Tokyo 1088639 Japan

Background: Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients. Results: the algorithms were tested with 56 data sets from four different species and compared to 14 other methods by the use of several coefficients both at nucleotide and site level. the results of our first approach showed a performance comparable to the other methods studied, especially at site level and in sensitivity scores, in which it scored better than any of the 14 existing tools. In the case of our previous algorithm, the new approach with the addition of the perplexity measurement clearly outperformed all of the other methods in sensitivity, both at nucleotide and site level, and in overall performance at site level. Conclusions: the statistics obtained show that the performance of a motif finding method based on the use of a CTM is satisfying enough to conclude that the application of topic models is a valid method for developing motif finding algorithms. Moreover, the addition of topic models to a previously developed method dramatically increased its performance, suggesting that this combined algorithm can be a useful tool to successfully predict motifs in different ki

关键词： Transcription Factor Binding Site Topic Model Nucleotide Level Motif Finding Site Level

来源：评论

学校读者我要写书评

暂无评论

Implementation of Grey Scale Normalization in Machine Learning & Artificial Intelligence for bioinformatics using Convolutional Neural Networks 6

Implementation of Grey Scale Normalization in Machine Learni...

引用

6th international conference on Inventive Computation Technologies (ICICT)

作者： Kothari, Divya Patel, Mayank Sharma, Ajay Kumar Geetanjali Inst Tech Studies Udaipur Rajasthan India

ISBN: (纸本)9781728185019

Machine Learning is a trending field nowadays and is very well known as an application of Artificial Intelligence (AI). Machine learning makes use of secure arithmetical algorithms to construct computers assignment in a constructive way without being unequivocally programmed. the algorithms acquire freedom of a participated value and estimate output for this by utilizing definite arithmetical methods. the key function of machine learning is to create smart machines that can visualize and work related to human beings. Artificial Intelligence has been witnessing a massive improvement in bridging the gap among the capabilities of humans and technologies. Similarly, one of the characteristics of the field were tried to combine the outstanding effects. A Convolution Neural Network (CNN) is a sort of Deep Learning algorithm which can obtain an input image, assign consequence to different aspects in the image and capable to differentiate one from the other. the pre-processing requirement in a CNN is lesser as compared to other classification algorithms. CNN has the capability to find out these filter's characteristics. bioinformatics is a term that is a mixture of two terms bio and informatics. Bio means associated with biology and informatics means in a sequence of information. thus bioinformatics is an area that deals with handing out and accepting biological statistics using the computational and arithmetical approaches. Machine Learning has added up to of applications in the area of bioinformatics. Machine Learning finds its submission in the subsequent subfields of bioinformatics. the aim of this article is to perform a grayscale normalization of a selected image and thereafter to reduce the effect of illumination differences. Normalization is considered so that CNN works in a faster manner. Different models are available but Keras model is selected to perform this task. Keras supports the style of data preparation for image statistics via the Image Data Generator gro

关键词： Machine Learning Artificial Intelligence Convolution Neural Network Application Program Interface

来源：评论

学校读者我要写书评

暂无评论

bioinformatics strategies for identifying regions of epigenetic deregulation associated with aberrant transcript splicing and RNA-editing 6

Bioinformatics strategies for identifying regions of epigene...

引用

6th international conference on bioinformatics models, methods and algorithms, bioinformatics 2015

作者： Champion, Mia D. Hlady, Ryan A. Yan, Huihuang Evans, Jared Nie, Jeff Lee, Jeong-Heon Bogenberger, James Nandakumar, Kannabiran Davila, Jaime Moore, Raymond Nair, Asha O'Brien, Daniel Zhu, Yuan-Xiao Kortum, K. Martin Ordog, Tamas Zhang, Zhiguo Joseph, Richard W. Stewart, A. Keith Kocher, Jean-Pierre Jonasch, Eric Robertson, Keith D. Tibes, Raoul Ho, thai H. Division of Biomedical Statistics and Informatics Mayo Clinic ScottsdaleAZ United States Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic RochesterMN United States Division of Biomedical Statistics and Informatics Mayo Clinic RochesterMN United States Translational Program Center for Individualized Medicine Mayo Clinic RochesterMN United States Division of Hematology and Oncology Mayo Clinic ScottsdaleAZ United States Department of Physiology and Biomedical Engineering Mayo Clinic RochesterMN United States Department of Biochemistry and Molecular Biology Mayo Clinic RochesterMN United States Division of Hematology and Oncology Mayo Clinic JacksonvilleFL United States Department of Genitourinary Medical Oncology University of Texas MD Anderson Cancer Center HoustonTX United States

ISBN: (纸本)9789897580703

Epigenetic modifications are associated with the regulation of co/post-transcriptional processing and differential transcript isoforms are known to be important during cancer progression. It remains unclear how disruptions of chromatin-based modifications contribute to tumorigenesis and how this knowledge can be leveraged to develop more potent treatment strategies that target specific isoforms or other products of the co/post-transcriptional regulation pathway. Rapid developments in all areas of next-generation sequencing (DNA, RNA-seq, ChIP-seq, Methyl-CpG, etc.) have provided new opportunities to develop novel integration and data-mining approaches, and also allows for exciting hypothesis driven bioinformatics and computational studies. Here, we present a program that we developed and summarize the results of applying our methods to analyze datasets from patient matched tumor or normal (T/N) paired samples, as well as cell lines that were either sensitive or resistant (S/R) to treatment with an anti-cancer drug, 5-Azacytidine (http://***/projects/chiprnaseqpro/). We discuss additional options for user-defined approaches and general guidelines for simultaneously analyzing and annotating epigenetic and RNA-seq datasets in order to identify and rank significant regions of epigenetic deregulation associated with aberrant splicing and RNA-editing.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Indexing k-mers in Linear-space for Quality Value Compression 10

Indexing k-mers in Linear-space for Quality Value Compressio...

引用

12th international Joint conference on Biomedical Engineering Systems and Technologies / 10th international conference on bioinformatics models, methods and algorithms (bioinformatics)

作者： Shibuya, Yoshihiro Comin, Matteo Univ Padua Dept Informat Engn Via Gradenigo 6B Padua Italy Univ Paris Est Marne la Vallee Lab Informat Gaspard Monge LIGM Batiment Copernic 5Bd Descartes Champs Sur Marne France

ISBN: (纸本)9789897583537

Many bioinformatics tools heavily rely on k-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive k-mer dictionaries are very memory inefficient, requiring very large amount of storage space to save each k-mer. this problem is generally worsened by the necessity of an index for fast queries. In this work we discuss how to build an indexed linear reference containing a set of input k-mers, and its application to the compression of quality score in FASTQ files. Most of the entropy of sequencing data lies in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNPs calling. We show how a dictionary of significant k-mers, obtained from SNPs databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value.

关键词： k-mers Indexing Quality Score Read Compression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：