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2011 ACM Conference on bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Sprecher, Emmett Sarkar, Sudipa Kleinstein, Steven H. Narayan, Murli Winer, Eric Tuck, David Lezon-Geyda, Kimberly Krop, Ian Harris, Lyndsay Department of Pathology Pathology Informatics United States Interdepartmental Program in Computational Biology and Bioinformatics United States Medical Oncology Yale Cancer Center United States School of Medicine Yale University New Haven CT United States Department of Medical Oncology Dana-Farber Cancer Institute Boston MA United States

ISBN: (纸本)9781450307963

Human epidermal growth factor receptor 2 amplified (HER2+) breast cancer is common and aggressive. Trastuzumab is a targeted therapy to the HER2 cell surface receptor and has greatly improved prognosis for HER2+ breast cancer patients, but trastuzumab-resistance is a significant problem. Few biomarkers of trastuzumab resistance of response have been found, and there has been tremendous difficulty identifying in vitro biomarkers that transfer to the in vivo HER2+ breast cancer setting. We identified gene expression signatures of trastuzumabresponse and trastuzumab-resistance in HER2+ breast cancer cell lines across 24-hour exposure to trastuzumab. A set of 180-genes changed significantly in a trastuzumab-sensitive HER2+ cell line (BT474) and 58 genes changed significantly across treatment in a trastuzumab-resistant HER2+ cell line (UACC812). To demonstrate that these signatures have clinical relevance, we applied them in vivo to gene expression profiles from early stage HER2+ breast tumors treated with a single dose of trastuzumab. We found that the BT474 trastuzumabresponsive signature was enriched among the changes in expression across treatment for responsive patients. The expression change for the BT474 signature genes was also able to partially cluster responsive and resistant breast tumors by outcome. A subset of the UACC812 trastuzumab-resistance signature was also enriched in the differential between responsive and resistant tumors prior to trastuzumab treatment. These results show for the first time that cell line-derived gene signatures can be useful in identifying whether breast cancer patients will ultimately benefit from trastuzumab treatment. This approach might one day be applied to enhance clinical treatment decisions. Copyright © 2011 ACM.

关键词： Gene expression

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A linked data graph approach to integration of immunological data

A linked data graph approach to integration of immunological...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Syed Ahmad Chan Bukhari Jeff Mandell Steven H. Kleinstein Kei-Hoi Cheung Division of Computer Science Mathematics and Science Lesley H. & William L. Collins College of Professional Studies St. John's University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Pathology Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Emergency Medicine and Yale Center for Medical Informatics Computationa Biology and Bioinformaticcs Yale University

ISBN: (纸本)9781728118680

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

关键词： ontology knowledgebase graph database immunology influenza vaccine graphic library Knowledge Bases Influenza Vaccines Ontology Immunology data integration Passwords DNA Integration integration data types Data sources SQL

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Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets

Hyperbolic Diffusion Procrustes Analysis for Intrinsic Repre...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ya-Wei Eileen Lin Yuval Kluger Ronen Talmon Viterbi Faculty of Electrical and Computer Engineering Technion Interdepartmental Program in Computational Biology and Bioinformatics Yale University Applied Math Program Yale University Department of Pathology School of Medicine Yale University

In this paper, we present Hyperbolic Diffusion Procrustes Analysis (HDPA), a new method for informative representation of hierarchical datasets based on hyperbolic geometry, diffusion geometry, and Procrustes analysis. Our method jointly embeds multiple datasets in a product manifold of hyperbolic spaces, where the data's hidden common hierarchical structure is provably recovered. In addition, our method generates an intrinsic embedding that accommodates the joint representation of multiple datasets with different features, acquired by different equipment, at different sites, or under different environmental conditions. Experimental results demonstrate the efficacy of HDPA on three biomedical datasets comprising heterogeneous gene expression and mass cytometry data.

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Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

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Lecture Notes in Computer Science

作者： Patro, Rob Sefer, Emre Malin, Justin Marçais, Guillaume Navlakha, Saket Kingsford, Carl Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States Department of Computer Science University of Maryland College Park MD 20742 United States Computational Biology Bioinformatics and Genomics Concentration Biological Sciences Graduate Program University of Maryland College Park MD 20742 United States Program in Applied Mathematics Statistics and Scientific Computation University of Maryland College Park MD 20742 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642230370

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

关键词： Genes

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Developmental systems biology flourishing on new technologies

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Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

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A pathway-based kernel boosting method for sample classification using genomic data

arXiv

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arXiv 2018年

作者： Zeng, Li Yu, Zhaolong Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States

The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classifica- tion loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites, and metastasis status. Our results show that PKB outperforms other methods, and identifies pathways relevant to the outcome variables. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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AI solutions for drafting in Magic: The gathering

arXiv

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arXiv 2020年

作者： Ward, Henry N. Brooks, Daniel J. Troha, Dan Mills, Bobby Khakhalin, Arseny S. Bioinformatics and Computational Biology Graduate Program University of Minnesota MinneapolisMN United States *** San DiegoCA United States Biology Program Bard College Annandale-on-HudsonNY United States

Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from ***. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Study of Classification Techniques Based on Spike Protein Sequences of MERS-CoV 23

A Study of Classification Techniques Based on Spike Protein ...

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Proceedings of the 2023 10th International Conference on Biomedical and bioinformatics Engineering

作者： Hayeon Kim Myeongji Cho Hyeon S. Son Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea and Institute of Health and Environment Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Institute of Health and Environment Seoul National University Republic of Korea and Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Republic of Korea

ISBN: (纸本)9798400708343

MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and variants. Previous studies have discussed the possibility of frequent recombination of MERS-CoV. We thus present a highly accurate method for the phylogenetic analysis and classification of MERS-CoV including recombinant sequences. We collected the sequences of S protein from MERS-CoV and divided them into five phylogenetic groups, of which recombinant sequences were divided into seven types. Physicochemical properties of amino acids were then calculated from the S protein sequences, and the results were used for the random forest model, Naïve Bayes classification, and k-nearest neighbor method. We also constructed several feature subsets based on the ranked amino acid properties and applied them to the random forest model. In each dataset, the amino acid physicochemical properties were ranked differently. Using this information, classification of MERS-CoV based on machine learning algorithms showed that the random forest model had the best accuracy and area under the curve compared with the k-nearest neighbor and Naïve Bayes classification methods. Several feature subsets were constructed using the correlation feature selection algorithm and applied to the random forest model. Overall, the performance of the classifier was improved compared to that when using all features. Coronaviruses including MERS-CoV continue to evolve into new forms through recombination or mutation. We thus present a method to increase the accuracy of their classification using additional information of the viral protein sequence, and confirm that a subunit consisting of optimal prominent features can improve the performance of the classifier by removing the unnecessary characteristic information.

关键词： MERS-CoV classification machine learning

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Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models

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Journal of Structural and Functional Genomics 2011年第2期12卷 137-147页

作者： Zimmermann, Michael T. Leelananda, Sumudu P. Gniewek, Pawel Feng, Yaping Jernigan, Robert L. Kloczkowski, Andrzej Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011-0320 United States L.H.Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50011-3020 United States Bioinformatics and Computational Biology Interdepartmental Graduate Program Iowa State University Ames IA 50011 United States Laboratory of Theory of Biopolymers Faculty of Chemistry University of Warsaw 02-093 Warsaw Pasteura 1 Poland Battelle Center for Mathematical Medicine Research Institute at Nationwide Children's Hospital Columbus OH 43205 United States Department of Pediatrics Ohio State University College of Medicine Columbus OH 43205 United States

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection. © 2011 Springer Science+Business Media B.V.

关键词： 4-Body potentials Elastic network models Multibody potentials Protein dynamics Protein structure prediction Protein structure refinement

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Characterization of the retinal proteome during rod photoreceptor genesis

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BMC Research Notes 2010年第1期3卷 1-8页

作者： Barnhill, Alison E Hecker, Laura A Kohutyuk, Oksana Buss, Janice E Honavar, Vasant G Greenlee, Heather West Interdepartmental Neuroscience Program Iowa State University Ames IA United States Department of Biomedical Sciences Iowa State University Ames IA United States Bioinformatics and Computational Biology Program Iowa State University Ames IA United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA United States Department of Computer Science Iowa State University Ames IA United States National Animal Disease Center Ames IA 50010 United States Mayo Clinic Department of Ophthalmology Rochester MN 55905 United States

Background. The process of rod photoreceptor genesis, cell fate determination and differentiation is complex and multi-factorial. Previous studies have defined a model of photoreceptor differentiation that relies on intrinsic changes within the presumptive photoreceptor cells as well as changes in surrounding tissue that are extrinsic to the cell. We have used a proteomics approach to identify proteins that are dynamically expressed in the mouse retina during rod genesis and differentiation. Findings. A series of six developmental ages from E13 to P5 were used to define changes in retinal protein expression during rod photoreceptor genesis and early differentiation. Retinal proteins were separated by isoelectric focus point and molecular weight. Gels were analyzed for changes in protein spot intensity across developmental time. Protein spots that peaked in expression at E17, P0 and P5 were picked from gels for identification. There were 239 spots that were picked for identification based on their dynamic expression during the developmental period of maximal rod photoreceptor genesis and differentiation. Of the 239 spots, 60 of them were reliably identified and represented a single protein. Ten proteins were represented by multiple spots, suggesting they were post-translationally modified. Of the 42 unique dynamically expressed proteins identified, 16 had been previously reported to be associated with the developing retina. Conclusions. Our results represent the first proteomics study of the developing mouse retina that includes prenatal development. We identified 26 dynamically expressed proteins in the developing mouse retina whose expression had not been previously associated with retinal development. © 2010 Greenlee et al.;licensee BioMed Central Ltd.

关键词： Protein Spot Mouse Retina Retinal Development Retinal Protein Probability Base Mowse Score

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