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Congress on Evolutionary Computation

作者： Ji-Hoon Lee Bado Lee Joon Shik Kim Russell Deaton Byoung-Tak Zhang Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea School of Computer Science & Engineering Seoul National University Seoul South Korea Biointelligence Laboratory Seoul National University Seoul South Korea Department of Computer Science and Engineering University of Arkansas Fayetteville USA Biointelligence Laboratory School of Computer Science & Engineering Seoul National University Seoul South Korea

We describe a "molecular" evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.

关键词： DNA Libraries Training computers Evolutionary computation In vitro Estimation

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A scalable approach for alignments in fuzzy biological networks

A scalable approach for alignments in fuzzy biological netwo...

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2010 International Conference on bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010

作者： Liu, Yunkai Verone, Alissa Bioinformatics Program Computer and Information Science Department Gannon University United States

ISBN: (纸本)9781617820694

Interactions between biological entities, whether cells, proteins or genes are under meticulous study. As high throughput calculations have made biological network a popular target for study, methods for comparison have not been mastered. Multiple classes of bionetwork models exist, such as metabolic, protein-gene, or protein-protein interactions;the latter being of most importance to our research. Several different computational approaches have been taken by introducing bionetworks as tools to evaluate genomic or biological data. The primary focus was to develop a new method for comparison of bionetworks with great amount of noises. In this paper, an algorithm was designed for the pairwise comparison of bionetworks, considering the most conserved and most variable areas. Copyright© (2010) by the International Society for Research in science and Technology.

关键词： Genes

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Super-resolution of mammograms

Super-resolution of mammograms

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2010 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB 2010

作者： Zheng, Jun Fuentes, Olac Leung, Ming-Ying Department of Computer Science University of Texas at El Paso El Paso TX United States Department of Mathematical Sciences Bioinformatics Program University of Texas at El Paso El Paso TX United States

ISBN: (纸本)9781424467662

High-quality mammography is the most effective technology presently available for breast cancer screening. High resolution mammograms usually lead to more accurate diagnoses;however, they require large doses of radiation, which may have harmful effects. In this paper, we present a method to synthesize high-resolution mammograms from low-resolution inputs, which offers the potential of allowing accurate diagnoses while minimizing risks to patients. Our algorithm combines statistical machine learning methods and stochastic search to learn the mapping from low-resolution to high-resolution mammograms using a large dataset of training image pairs. Experimental results show that the super-resolution algorithm can generate high-quality, high-resolution breast mammograms from low-resolution input with no human intervention. © IEEE.

关键词： Optical resolving power

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A scalable method for detecting multiple loci associated with traits using TF-IDF weighting and association rule mining

A scalable method for detecting multiple loci associated wit...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Sunwon Lee Jaewoo Kang Junho Oh Department of Graduate Program in Bioinformatics Korea University Seoul South Korea Department of Computer Science and Engineering Korea University Seoul South Korea

The recent advance in SNP genotyping has made a significant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase of the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address the issue, we propose a method that exploits two well-tested models: the document-term model and the transaction analysis model. The proposed method consists of two phases. In the first phase, we reduce the dimensions of the SNP genotype data by extracting significant SNPs through transformation of the data in lieu of the document-term model. In the second phase, we discover the association rules that signify the relations between the SNPs and the traits, through the application of the transactional analysis in the reduced-dimension genotype data. We validated the discovered rules through the literature survey. Experiments were also carried out using the HGDP panel data provided by the Foundation Jean Dausset-CEPH, which prove the validity of our new method for identifying appropriate dimensional reduction and associations of multiple SNPs and traits.

关键词： Association rules Data models Analytical models Diseases Asia Context

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Multistate modeling and simulation for regulatory networks 10

Multistate modeling and simulation for regulatory networks

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Simulation Winter Conference

作者： Zhen Liu Umme Juka Mobassera Clifford A. Shaffer Layne T. Watson Yang Cao Department of Computer Science Virginia Technology Blacksburg VA USA Program in Genetics Bioinformatics and Computational Biology Virginia Technology Blacksburg VA USA Department of Computer Science Department of Mathematics Virginia Technology Blacksburg VA USA

ISBN: (纸本)9781424498642

Many protein regulatory models contain chemical species best represented as having multiple states. Such models stem from the potential for multiple levels of phosphorylation or from the formation of multiprotein complexes. We seek to support such models by augmenting an existing modeling and simulation system. Interactions between multistate species can lead to a combinatorial explosion in the potential state space. This creates a challenge when using Gillespie's stochastic simulation algorithm (SSA). Both the network-free algorithm (NFA) and various rules-based methods have been proposed to more efficiently simulate such models. We show how to further improve NFA to integrate population-based and particle-based features. We then present a population-based scheme for the stochastic simulation of rule-based models. A complexity analysis is presented comparing the proposed simulation methods. We present numerical experiments for two sample models that demonstrate the power of the proposed simulation methods.

关键词： Mathematical model Biological system modeling Numerical models Chemicals Stochastic processes Equations Proteins

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Dynamic and static analysis of transcriptional regulatory networks in a hierarchical context

Dynamic and static analysis of transcriptional regulatory ne...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Nitin Bhardwaj Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Heaven CT USA Department of Computer Science Yale University New Heaven CT USA

Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures such as hierarchies. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. Here we study a wide range of regulatory networks (transcriptional, modification and phosphorylation) in a hierarchical context for five evolutionarily diverse species. We specify three levels of regulators - top, middle and bottom - which collectively regulate the non-regulator targets lying in the lowest fourth level, and we define quantities for nodes, levels and entire networks that measure their degree of collaboration and autocratic or democratic character. Overall we show that in all the networks studied, the middle level has the highest collaborative propensity and that co-regulatory partnerships occur most frequently amongst mid-level regulators, an observation that has parallels in efficient corporate settings where middle managers need to interact most to ensure organizational effectiveness. Then to study dynamic effects, superimpose the phenotypic effects of tampering with nodes and edges directly onto the hierarchies. We reconstruct modified hierarchies reflecting changes in the level of regulators within the hierarchy upon deletions or insertions of nodes or edges. Overall, we find that rewiring events that affect upper levels have a more dramatic effect on cell proliferation rate and survival than do those involving lower levels. We also investigate other features connected to the importance of upper-level regulators: expression divergence, back-up copies and expression level.

关键词： Regulators Collaboration Context bioinformatics Biochemistry Molecular biophysics Performance analysis

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The keyword-based and semantic-driven data matching approach for assisting structuralizing the textual clinical documents

The keyword-based and semantic-driven data matching approach...

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International Conference on Biomedical Engineering and Informatics

作者： Chen, Chi-Huang Ping, Xiao-Ou Wang, Zi-Jun Hsieh, Sheau-Ling Chen, Li-Chin Tseng, Yi-Ju Hsu, Ching-Wei Lai, Feipei Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronic and Bioinformatics National Taiwan University Taipei Taiwan EECS International Graduate Program Institute/IT Center National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781424464968

The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches. ©2010 IEEE.

关键词： Semantics

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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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Analysis of cancer omics data in a semantic web framework

Analysis of cancer omics data in a semantic web framework

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Workshop on Semantic Web Applications and Tools for Life sciences 2010, SWAT4LS 2010

作者： Holford, Matthew E. McCusker, James P. Cheung, Kei-Hoi Krauthammer, Michael Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Department of Pathology Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States

Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancer cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Logic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.

关键词： Semantic Web

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chi-Huang Chen Sheau-Ling Hsieh Yung-Ching Weng Wen-Yung Chang Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan University Taiwan Institute of Computer Science and Engineering College of Computer Science National Chiao Tung University Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

关键词： Semantics Biomedical measurements Correlation Kernel Training Medical services Support vector machine classification

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