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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Krishnajith, Anaththa P. D. Kelly, Wayne Hayward, Ross Tian, Yu-Chu Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia

ISBN: (纸本)9781467358750

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

关键词： Correlation matrix bioinformatics computing scalable computing memory management phylogenetic analysis

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Early Lung Cancer Detection using Nucleus Segementation based Features

Early Lung Cancer Detection using Nucleus Segementation base...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Kancherla, Kesav Mukkamala, Srinivas New Mexico Inst Min & Technol ICASA CAaNES Socorro NM 87801 USA

ISBN: (纸本)9781467358750

In this study we propose an early lung cancer detection methodology using nucleus based features. First the sputum samples from patients are labeled with Tetrakis Carboxy Phenyl Porphine (TCPP) and fluorescent images of these samples are taken. TCPP is a porphyrin that is able to assist in labeling lung cancer cells by increasing numbers of low density lipoproteins coating on the surface of cancer. We study the performance of well know machine learning techniques in the context of lung cancer detection on Biomoda dataset. We obtained an accuracy of 81% using 71 features related to shape, intensity and color in our previous work. By adding the nucleus segmented features we improved the accuracy to 87%. Nucleus segmentation is performed by using Seeded region growing segmentation method. Our results demonstrate the potential of nucleus segmented features for detecting lung cancer.

关键词： Lung Cancer detection bioinformatics Machine Learning Seeded Region Growing segmentation

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Metabolite identification through multiple kernel learning on fragmentation trees

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bioinformatics 2014年第12期30卷 157-164页

作者： Shen, Huibin Duhrkop, Kai Bocker, Sebastian Rousu, Juho Aalto Univ Dept Informat & Comp Sci Espoo Finland Helsinki Inst Informat Technol Espoo Finland Univ Jena Chair Bioinformat Jena Germany

Motivation: Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints;predicted fingerprints, in turn, can be used to score candidate molecular structures. Results: Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list.

关键词： Algorithms Artificial intelligence Metabolomics/methods Molecular Structure Software Tandem Mass Spectrometry/methods

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Arithmetic computation using self-assembly of DNA tiles: Integer power over finite field GF(2n)

Arithmetic computation using self-assembly of DNA tiles: Int...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Yongnan Li Limin Xiao Department of Policing Intelligence Chinese People's Public Security University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781479956708

DNA-based cryptography is a new developing interdisciplinary area which combines cryptography, mathematical modeling, biochemistry and molecular biology. It is still an open question that how to implement the arithmetic operations used in cryptosystem based on DNA computing. This paper proposes a DNA computing model to compute integer power over finite field GF(2 n ). The computation tiles performing five different functions assemble into the seed configuration with inputs to figure out the result. It is given that how the computation tiles be coded in bits and how assembly rules work. The assembly time complexity is 2n 2 +n-1 and the space complexity is n 4 +n 3 . This model requires 6436 types of computation tiles and 12 types of boundary tiles.

关键词： Assembly computational modeling DNA DNA computing Self-assembly Mathematical model Conferences

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Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

Overcoming the Scalability Challenges of Epidemic Simulation...

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International symposium on Parallel and Distributed Processing (IPDPS)

作者： Jae-Seung Yeom Abhinav Bhatele Keith Bisset Eric Bohm Abhishek Gupta Laxmikant V. Kale Madhav Marathe Dimitrios S. Nikolopoulos Martin Schulz Lukasz Wesolowski Virginia Bioinformatics Institute Virginia Tech Blacksburg VA USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA University of Illinois at Urbana-Champaign Urbana IL US Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA School of EEECS Queen's University of Belfast Belfast Northern Ireland

ISBN: (纸本)9781479938018

Modeling dynamical systems represents an important application class covering a wide range of disciplines including but not limited to biology, chemistry, finance, national security, and health care. Such applications typically involve large-scale, irregular graph processing, which makes them difficult to scale due to the evolutionary nature of their workload, irregular communication and load imbalance. EpiSimdemics is such an application simulating epidemic diffusion in extremely large and realistic social contact networks. It implements a graph-based system that captures dynamics among co-evolving entities. This paper presents an implementation of EpiSimdemics in Charm++ that enables future research by social, biological and computational scientists at unprecedented data and system scales. We present new methods for application-specific processing of graph data and demonstrate the effectiveness of these methods on a Cray XE6, specifically NCSA's Blue Waters system.

关键词： computational modeling Scalability Load modeling Load management Sociology Statistics Educational institutions

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Critical Junction: Nonlinear Dynamics, Swarm intelligence and Cancer Research

Critical Junction: Nonlinear Dynamics, Swarm Intelligence an...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Rosenfeld, Simon NCI Canc Prevent Div Bethesda MD 20892 USA

ISBN: (纸本)9781467358750

Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Despite astoundingly wide repertoire of observed forms, there are comparatively simple rules governing evolution of large systems towards self-organization, in general, and towards swarm intelligence, in particular. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) allowing for unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, to social networks, and to many other systems. The GCT provides a conceptual basis for understanding the emergent phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. A growing body of evidence confirms the premise that disruption of quorum sensing, an important aspect of swarm intelligence, plays a key role in carcinogenesis. Other aspects of swarm intelligence, such as collective memory, adaptivity (a form of learning from experience) and ability for self-repair are the key for understanding biological robustness and acquired chemoresistance. Yet another aspects of swarm intelligence, such as division of labor and competitive differentiation, may be helpful in understanding of cancer compartmentalization and tumor heterogene

关键词： Global Consensus Theorem Swarm intelligence Biomolecular Networks Carcinogenesis

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Data Mining Supported Analysis of Medical Atomic Force Microscopy Images

Data Mining Supported Analysis of Medical Atomic Force Micro...

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ieee symposium on computational intelligence in Healthcare and e-Health (CICARE)

作者： Hutterer, Stephan Mayr, Sandra Zauner, Gerald Silye, Rene Schilcher, Kurt Univ Appl Sci Upper Austria Sch Engn & Environm Sci Wels Austria Univ Appl Sci Upper Austria Sch Appl Hlth & Social Sci Linz Austria Neuropathic Hosp Wagner Jauregg Dept Pathol Linz Austria

ISBN: (纸本)9781467358835

Taking a look at actual developments in the field of bioinformatics, computer support in different fields of medicine and biology is an increasing field of interest. Especially for recognition and classification of various kinds of diseases, researchers already identified the usage of data mining techniques as supporting tool for computer supported analysis and diagnosis. In order to build such tools, highly accurate and machine-readable data is needed. Therefore, atomic force microscopy (AFM) is shown within this paper as measurement technology, that provides true 3-D data of any tissue and thus is able to build the fundament for further computational processing steps. Within two practical examples dealing with brain tumor tissue on the one hand and myocardial muscle tissue on the other hand, data mining supported analysis of AFM images will be demonstrated. The combination of data mining techniques with AFM measurements at nanoscale therefore forms a promising fundament for future computer enabled support systems in medicine and biology.

关键词： atomic force microscopy bioinformatics biomedical optical imaging brain computer aided analysis data mining diseases image classification medical image processing muscle pattern recognition tumours

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Link-based Identification of Survival Time-related Biological Pathways

Link-based Identification of Survival Time-related Biologica...

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ieee International Conference on bioinformatics and Biomedicine

作者： Hong-Mei Zheng Gao-Jian Jing Zirui Zhang Hong-Qiang Wang School of Mechanical and Automotive Engineering Hefei University of Technology Machine Intelligence & Computational Biology Lab Institute of Intelligent Machines Chinese Academy of Sciences

ISBN: (纸本)9781479956708

This paper proposes a novel survival time pathway hunting method based on gene links. In the method, we incorporate gene link information for testing how significantly a pathway is associated with cancer patient's survival. Specifically, we establish a link-based Cox proportional hazard model (Link-Cox), in which two linked genes are considered together as a link variable and the association with survival time is assessed using Cox proportional hazard model. Based on the Link-Cox model, A new statistic for measuring the association of a pathway with survival time, named pathway survival score (PSS), is formulated by summarizing survival significance over all the gene links in the pathway. We also devise a permutation test to estimate the significance of an observed PSS. To evaluate the proposed method, we applied it to two publicly available lung cancer gene expression data sets. Experimental results on two real datasets show the effectiveness and efficiency of the proposed method.

关键词： Pathway analysis Gene link Cox proportional hazard model Gene expression data Survival time

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Thermodynamic Post-Processing versus GC-Content Pre-Processing for DNA Codes Satisfying the Hamming Distance and Reverse-Complement Constraints

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2014年第2期11卷 441-452页

作者： Tulpan, Dan Smith, Derek H. Montemanni, Roberto Natl Res Council Canada Moncton NB E1A 7R1 Canada Univ Moncton Dept Biol Moncton NB E1A 3E9 Canada Univ South Wales Div Math & Stat Pontypridd CF37 1DL M Glam Wales Univ Appl Sci Southern Switzerland SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Manno Switzerland

Stochastic, meta-heuristic and linear construction algorithms for the design of DNA strands satisfying Hamming distance and reverse-complement constraints often use a GC-content constraint to pre-process the DNA strands. Since GC-content is a poor predictor of DNA strand hybridization strength the strands can be filtered by post-processing using thermodynamic calculations. An alternative approach is considered here, where the algorithms are modified to remove consideration of GC-content and rely on post-processing alone to obtain large sets of DNA strands with satisfactory melting temperatures. The two approaches (pre-processing GC-content and post-processing melting temperatures) are compared and are shown to be complementary when large DNA sets are desired. In particular, the second approach can give significant improvements when linear constructions are used.

关键词： DNA design linear codes stochastic local search Hamming distance reverse-complement

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Integrated Analysis of Gene Expression and Genome-wide DNA Methylation for Tumor Prediction: An Association Rule Mining-based Approach

Integrated Analysis of Gene Expression and Genome-wide DNA M...

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10th Annual ieee symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Mallik, Saurav Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Univ Kalyani Dept Syst & Comp Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

ISBN: (纸本)9781467358750

Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.

关键词： Gene expression DNA methylation statistical tests association rule mining Genetic Algorithm (GA) based rank aggregation rule-in terestingness measures

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