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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81-90 订阅

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

Bioinformatics strategies for identifying regions of epigene...

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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

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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 ...

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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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Choosing an Appropriate Ensemble Classifier for Balanced bioinformatics Data 16

Choosing an Appropriate Ensemble Classifier for Balanced Bio...

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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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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...

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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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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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Visual comparison of 3D medical image segmentation algorithms based on statistical shape models 6th

Visual comparison of 3D medical image segmentation algorithm...

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6th international conference on Digital Human Modeling, DHM 2015 Held as Part of 17th international conference on Human-Computer Interaction, HCI international 2015

作者： Geurts, Alexander Sakas, Georgios Kuijper, Arjan Becker, Meike von Landesberger, Tatiana TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783319210698

3D medical image segmentation is needed for diagnosis and treatment. As manual segmentation is very costly, automatic segmentation algorithms are needed. For finding best algorithms, several algorithms need to be evaluated on a set of organ instances. this is currently difficult due to dataset size and complexity. In this paper, we present a novel method for comparison and evaluation of several algorithms that automatically segment 3D medical images. It combines algorithmic data analysis with interactive data visualization. A clustering algorithm identifies regions of common quality across the segmented data set for each algorithm. the comparison identifies best algorithms per region. Interactive views show the algorithm quality. We applied our approach to a real-world cochlea dataset, which was segmented with several algorithms. Our approach allowed segmentation experts to compare algorithms on regional level and to identify best algorithms per region. © Springer international Publishing Switzerland 2015.

关键词： Clustering algorithms

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Compare local pocket and global protein structure models by small structure patterns 15

Compare local pocket and global protein structure models by ...

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

作者： Cui, Xuefeng Kuwahara, Hiroyuki Li, Shuai Cheng Gao, Xin Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Computer Science City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781450338530

Researchers proposed several criteria to assess the quality of predicted protein structures because it is one of the essential tasks in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions. Popular criteria include root mean squared deviation (RMSD), MaxSub score, TM-score, GDT-TS and GDT-HA scores. All these criteria require calculation of rigid transformations to superimpose the the predicted protein structure to the native protein structure. Yet, how to obtain the rigid transformations is unknown or with high time complexity, and, hence, heuristic algorithms were proposed. In this work, we carefully design various small structure patterns, including the ones specifically tuned for local pockets. Such structure patterns are biologically meaningful, and address the issue of relying on a sufficient number of backbone residue fragments for existing methods. We sample the rigid transformations from these small structure patterns;and the optimal superpositions yield by these small structures are refined and reported. As a result, among 11;669 pairs of predicted and native local protein pocket models from the CASP10 dataset, the GDT-TS scores calculated by our method are significantly higher than those calculated by LGA. Moreover, our program is computationally much more efficient. Source codes and executables are publicly available at http://***/prosta/. Copyright is held by the author/owner(s).

关键词： Proteins

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6th international conference on Information Technology in Bio-and Medical Informatics, ITBAM 2015

6th International Conference on Information Technology in Bi...

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6th international conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015

ISBN: (纸本)9783319227405

the proceedings contain 10 papers. the special focus in this conference is on Medical Terminology, Clinical Processes and Machine Learning in Biomedicine. the topics include: Exploiting PubMed to answer biomedical questions in natural language;using twitter data and sentiment analysis to study diseases dynamics;an open data approach for clinical appropriateness;a logistic regression approach for identifying hot spots in protein interfaces;the discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation;data mining techniques in health informatics;artificial neural networks in diagnosis of liver diseases;how to increase the effectiveness of the hepatitis diagnostics by means of appropriate machine learning methods;ant-inspired algorithms for decision tree induction;microsleep classifier using EOG channel recording.

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Prediction models for estimation of survival rate and relapse for breast cancer patients 15

Prediction models for estimation of survival rate and relaps...

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15th IEEE international conference on bioinformatics and Bioengineering, BIBE 2015

作者： Cirkovic, Bojana R. Andjelkovic Cvetkovic, Aleksandar M. Ninkovic, Srdjan M. Filipovic, Nenad D. Faculty of Engineering University of Kragujevac Serbia Clinic for Surgery Clinical Center of Kragujevac Serbia Faculty of Medical Sciences University of Kragujevac Serbia

ISBN: (纸本)9781467379830

In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. these algorithms were successfully applied to a novel breast cancer data set of the Clinical Center of Kragujevac. the Naive Bayes classifier is selected as a model for prognosis of cancer survivability on the basis of the 5 years survival rate, while the Artificial Neural Network has achieved the best performance in prognosis of cancer recurrence. Selection of twenty attributes that are the most related to success of prognosis on survivability can give new insights into the set of prognostic factors which need to be observed by medical experts. © 2015 IEEE.

关键词： Diseases

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