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检索条件"任意字段=6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015"

共 237 条记录，以下是11-20 订阅

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Does the Inclusion of Data Sampling Improve the Performance of Boosting algorithms on Imbalanced bioinformatics Data? 14

Does the Inclusion of Data Sampling Improve the Performance ...

引用

IEEE 14th international conference on Machine Learning and Applications ICMLA

作者： Fazelpour, Alireza Khoshgoftaar, Taghi M. Dittman, David J. Napolitano, Amri Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9781509002870

bioinformatics datasets contain many challenging characteristics, such as class imbalance, which adversely impacts the performance of supervised classification models built on these datasets. Techniques such as ensemble learning and data sampling from the domain of data mining can be deployed to alleviate the problem and to improve the classification performance. In this study, we sought to seek whether inclusion of data sampling within the ensemble framework can further improve the performance of classification models. To this end, we performed an experimental study using two newly hybrid ensemble techniques, one integrates feature selection within the boosting process and the other incorporates random under-sampling followed by feature selection within the boosting framework, two learners, three forms of feature rankers, and four feature subset sizes on 15 highly imbalanced bioinformatics datasets. Our results and statistical analysis demonstrate that the difference between the two boosting methods is statistically insignificant. therefore, as the inclusion of data sampling has no significant positive effect on the performance of ensemble classifiers, it is not required to achieve maximum classification performance. To our knowledge, this is the first empirical study that examined the effects of data sampling, random under-sampling, to enhance classification performance of boosting algorithm for highly imbalanced bioinformatics data.

关键词： Boosting data sampling ensemble learning class imbalance bioinformatics

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Backward Pattern Matching on Elastic Degenerate Strings 12

Backward Pattern Matching on Elastic Degenerate Strings

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14th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 12th international conference on bioinformatics models, methods and algorithms (bioinformatics)

作者： Prochazka, Petr Cvacho, Ondrej Krcal, Lubos Holub, Jan Czech Tech Univ Fac Informat Technol Dep Theoret Comp Sci Thakurova 2700-9 Prague 6 Czech Republic

ISBN: (纸本)9789897584909

Recently, the concept of Elastic Degenerate Strings (EDS) was introduced as a way of representing a sequenced population of the same species. Several on-line Elastic Degenerate String Matching (EDSM) algorithms were presented so far. Some of them provide practical implementation. We propose a new on-line EDSM algorithm BNDM-EDS. Our algorithm combines two traditional algorithms BNDM and the Shift-And that were adapted to the specifics needed by Elastic Degenerate Strings. BNDM-EDS is running in O(Nmd[m/w]) worst-case time. this implies O (Nm) time for small patterns, where m is the length of the searched pattern, N is the size of EDS, and w is the size of the computer word. the algorithm uses O (N + n) space, where n is the length of EDS. BNDM-EDS requires a simple preprocessing step with time and space O (m). Experimental results on real genomic data show superiority of BNDM-EDS over state-of-the-art algorithms.

关键词： Elastic Degenerate Strings Genomic Sequences Degenerate Pattern Matching Pan-Genomics

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Choosing an Appropriate Ensemble Classifier for Balanced bioinformatics Data 16

Choosing an Appropriate Ensemble Classifier for Balanced Bio...

引用

IEEE 16th international conference on Information Reuse and Integration

作者： Fazelpour, Alireza Khsohgoftaar, Taghi M. Dittman, David J. Napolitano, Amri Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9781467366564

bioinformatics datasets contain a number of characteristics, such as noisy data and difficult to learn class boundaries, which make it challenge to build effective predictive models. One option for improving results is the use of ensemble learning methods, which involve combining the results of multiple predictive models into a single decision. Since we do not rely on a single model, we reduce the effect of any hidden bias which may reside in a single model. In this study, we investigate two ensemble learning methods, Select-Bagging and Random Forest, to find which one is better suited for classification of bioinformatics data. In addition, we examine how the choice of learning algorithms affects the classification results of the Bagging method. We conduct an empirical study using six ensemble classifiers (Random Forest and Select-Bagging utilizing five different classifiers) applied to 12 balanced datasets using three feature rankers along with four feature subset sizes. Based on our results, including statistical analysis, we recommend Random Forest as it is competitive with the best of the Select-Bagging classifiers, and does not require an additional choice of classifier, which can significantly affect classification performance. To our knowledge, this work is unique in terms of investigating the effectiveness of these two ensemble learning methods in the domain of bioinformatics as well as examining how the choice of classifier impacts classification results when using a Bagging-based ensemble learning method.

关键词： Bagging Select-Bagging Random Forest (RF) classification classifier bioinformatics

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Functional site prediction selects correct protein models

Functional site prediction selects correct protein models

引用

6th international conference on bioinformatics, InCoB 2007

作者： Chelliah, Vijayalakshmi Taylor, William R. Division of Mathematical Biology National Institute for Medical Research The Ridgeway Mill Hill London NW7 1AA United Kingdom

Background: the prediction of protein structure can be facilitated by the use of constraints based on a knowledge of functional sites. Without this information it is still possible to predict which residues are likely to be part of a functional site and this information can be used to select model structures from a variety of alternatives that would correspond to a functional protein. Results: Using a large collection of protein-like decoy models, a score was devised that selected those with predicted functional site residues that formed a cluster. When tested on a variety of small α/β/α type proteins, including enzymes and non-enzymes, those that corresponded to the native fold were ranked highly. this performance held also for a selection of larger α/β/α proteins that played no part in the development of the method. Conclusion: the use of predicted site positions provides a useful filter to discriminate native-like protein models from non-native models. the method can be applied to any collection of models and should provide a useful aid to all modelling methods from ab initio to homology based approaches. © 2006 Brahmachary et al;licensee BioMed Central Ltd.

关键词： Enzymes

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Cancer Prediction using Clinical and Genomic Data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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6th international conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

this systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. the review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. the studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. the results show significant improvements in model prediction performance accuracy across multiple studies. the review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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Finite difference methods, theory and applications: 6th international conference, FDM 2014 Lozenetz, Bulgaria, June 18-23, 2014 revised selected papers 6th

Finite difference methods, theory and applications: 6th inte...

引用

6th international conference on Finite Difference methods, FDM 2014

作者： Dimov, Ivan Faragó, István Vulkov, Lubin Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Institute of Mathematics and MTA-ELTE Numerical Analysis and Large Networks Research Group Eötvös Loránd University Budapest Hungary University of Rousse Rousse Bulgaria

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Selecting the appropriate ensemble learning approach for balanced bioinformatics data 28

Selecting the appropriate ensemble learning approach for bal...

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28th international Florida Artificial Intelligence Research Society conference, FLAIRS 2015

作者： Dittman, David J. Khoshgoftaar, Taghi M. Napolitano, Amri Florida Atlantic University United States

ISBN: (纸本)9781577357308

Ensemble learning (process of combining multiple models into a single decision) is an effective tool for improving the classification performance of inductive models. While ideal for domains like bioinformatics with many challenging datasets, many ensemble methods, such as Bagging and Boosting, do not take into account the high-dimensionality (large number of features per instance) that is commonly found in bioinformatics datasets. this work seeks to observe the effects of two relatively new ensemble learning methods (Select-Bagging and Select-Boosting: the Bagging and Boosting approaches with feature selection implemented within each iteration of their algorithms) on a series of seven balanced (greater than a 43.50% minority class distribution) bioinformatics datasets. Additionally, we included the results when no ensemble approach is implemented (denoted as No-Ensemble) so that we can observe the full effects of ensemble learning. In order to test the three approaches we use three feature rankers, four feature subset sizes, and two classifiers. the results show that Select-Bagging is the top performing ensemble approach and statistical analysis confirms that Select-Bagging is significantly better than No-Ensemble and better (though not significantly) than Select-Boosting. Our recommendation is that Select- Bagging is an excellent choice for improving classification performance for bioinformatics datasets. To our knowledge, this work is the first empirical study focused exclusively on balanced bioinformatics datasets that investigated the effects of ensemble learning and utilizes Select-Bagging and introduces Select-Boosting. Copyright © 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： bioinformatics

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Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance

Aggregating Statistically Correlated Metabolic Pathways Into...

引用

13th international conference on bioinformatics models, methods and algorithms (bioinformatics) held as part of 15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Basher, Abdur Rahman M. A. Hallam, Steven J. Univ British Columbia Grad Program Bioinformat Vancouver BC V5Z 4S6 Canada Univ British Columbia Dept Microbiol & Immunol Vancouver BC V6T 1Z3 Canada

ISBN: (纸本)9789897585524

Metabolic pathway prediction from genomic sequence information is an essential step in determining the capacity of living things to transform matter and energy at different levels of biological organization. A detailed and accurate pathway map enables researchers to interpret and engineer the flow of biological information from genotype to phenotype in both organismal and multi-organismal contexts. In this paper, we propose two novel hierarchical mixture models, SOAP (sparse correlated pathway group) and SPREAT (distributed sparse correlated pathway group), to improve pathway prediction outcomes. Both models leverage pathway abundance to represent an organismal genome as a mixed distribution of groups, and each group, in turn, is a mixture of pathways. Moreover, both models deal with missing potential pathways in the training set by provisioning supplementary pathways into the learning framework as part of noise reduction efforts. Because the introduction of supplementary pathways may lead to overestimation of some pathways, dual sparseness is applied. the resulting pathway group dataset is then used to train multi-label learning algorithms. Model effectiveness was evaluated on metabolic pathway prediction where correlated models, in particular, SOAP was able to equal or exceed the performance of previous pathway prediction algorithms on organismal genomes.

关键词： Pathway Group Relabeling Data Augmentation Correlated models Metabolic Pathway Prediction MetaCyc

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An Approximate Execution of Rule-Based Multi-level models

An Approximate Execution of Rule-Based Multi-level Models

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11th international conference on Computational methods in Systems Biology (CMSB)

作者： Helms, Tobias Luboschik, Martin Schumann, Heidrun Uhrmacher, Adelinde M. Univ Rostock Inst Comp Sci D-18059 Rostock Germany

ISBN: (纸本)9783642407086;9783642407079

In cell biology, models increasingly capture dynamics at different organizational levels. therefore, new modeling languages are developed, e. g., like ML-Rules, that allow a compact and concise description of these models. However, the more complex models become the more important is an efficient execution of these models. tau-leaping algorithms can speed up the execution of biochemical reaction models significantly by introducing acceptable inaccurate results. Whereas those approximate algorithms appear particularly promising to be applied to hierarchically structured models, the dynamic nested structures cause specific challenges. We present a tau-leaping algorithm for ML-Rules which tackles these specific challenges and evaluate the efficiency and accuracy of this adapted tau-leaping based on a recently developed visual analysis technique.

关键词： computational biology rule-based modeling multi-level modeling tau-leaping efficient execution

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Sequential parallel LASSO models for eQTL analysis 15

Sequential parallel LASSO models for eQTL analysis

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6th ACM conference on bioinformatics, Computational Biology, and Health Informatics, BCB 2015

作者： He, Anhong Hall, Benika Wen, Jia Liang, Yingbin Shi, Xinghua Syracuse University 900 South Crouse Ave. SyracuseNY13244 United States University of North Carolina at Charlotte 9201 University City Blvd. CharlotteNC28223 United States

ISBN: (纸本)9781450338530

the availability of large-scale genomic and transcriptomic data on populations makes it necessary to perform computationally intensive expression quantitative trait locus (eQTL) analysis. Modeling in a sparse learning framework, LASSO based tools are powerful for eQTL analysis. However, classical LASSO becomes limited for big genomic data. We thus propose two novel methods, namely sequential LASSO and parallel LASSO, to conduct eQTL analysis for datasets of ultra-high dimension. We theoretically prove the consistency of our methods under mild conditions and perform extensive simulations on synthetic data to validate our methods. We also apply our methods to a real human genomics database demonstrate the application of our method. Copyright is held by the author/owner(s). Copyright is held by the author/owner(s).

关键词： Genome

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