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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Pavel Pevzner University of California San Diego

ISBN: (纸本)9781728118680

While some experimental biologists view bioinformatics as a servant, I argue that it is rapidly turning into the queen of molecular biology. I will illustrate this view by showing how recent computational developments brought down biological dogmas that remained unchallenged for at least three decades. Specifically, I will discuss the N-end theory connecting the protein half-life with N-terminal Methionine Excision, the Master Alu Theory explaining repeat proliferation in the human genome, and Random Breakage Model of genome rearrangements. In the second part of the talk, I will discuss a century-old dogma about the traditional classroom and describe the recent efforts to repudiate it using Intelligent Tutoring systems. I will describe a new educational technology called a Massive Adaptive Interactive Text (MAIT) that can prevent individual learning breakdowns and outperform a professor in a classroom. I will argue that computer science is a unique discipline where the transition to MAITs is about to happen and will describe a bioinformatics MAIT that has already outperformed me. In difference from existing Massive Online Open Courses (MOOCs), MAITs will capture digitized individual learning paths of all students and will transform educational psychology into a digital science. I will argue that the future MAIT revolution will profoundly affect the way we all teach and will generate large population-wide datasets containing individual learning paths through various MAITs.

关键词： bioinformatics biology computing educational courses genetics genomics intelligent tutoring systems molecular biophysics proteins teaching molecular biology bioinformatics Intelligent Tutoring systems biology computing Education courses Genomics Genetics queens Volition Educational Psychology Learning Human Genome Proteins iodine

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IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks

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ieee-ACM TRANSACTIONS ON computational BIOLOGY AND bioinformatics 2018年第4期15卷 1247-1258页

作者： Yerneni, Satwica Khan, Ishita K. Wei, Qing Kihara, Daisuke Mayo Clin Div Biomed Stat & Informat Rochester MN 55905 USA Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Purdue Univ Dept Biol Sci W Lafayette IN 47906 USA

Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI)network of an organism provides static yet an essential structure of interactions, which is valuable clue for understanding the functions of proteins and pathways. PPIs are determined primarily by experimental methods;however, computational PPI prediction methods can supplement or verify PPIs identified by experiment. Here, we developed a novel scoring method for predicting PPIs from Gene Ontology (GO) annotations of proteins. Unlike existing methods that consider functional similarity as an indication of interaction between proteins, the new score, named the protein-protein Interaction Association Score (IAS), was computed from GO term associations of known interacting protein pairs in 49 organisms. IAS was evaluated on PPI data of six organisms and found to outperform existing GO term-based scoring methods. Moreover, consensus scoring methods that combine different scores further improved performance of PPI prediction.

关键词： bioinformatics proteins computational systems biology biological interactions

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GPU Acceleration of Advanced k-mer Counting for computational Genomics 29

GPU Acceleration of Advanced <i>k</i>-mer Counting for Compu...

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29th Annual ieee International conference on Application-Specific systems, Architectures and Processors (ASAP)

作者： Li, Huiren Ramachandran, Anand Chen, Deming Univ Illinois Dept Elect & Comp Engn 1406 W Green St Urbana IL 61801 USA

ISBN: (纸本)9781538674796

k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. Compared to KMC2 running on a single CPU thread, our implementation using the GPU achieved 4.03x speedup when using one CPU thread, and 5.88x speedup when using four CPU threads. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.

关键词： Graphics processing units Instruction sets Sequential analysis Kernel bioinformatics Tools Acceleration"

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CIBCB 2016 - Annual ieee International conference on computational Intelligence in bioinformatics and computational Biology

CIBCB 2016 - Annual IEEE International Conference on Computa...

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13th ieee International conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2016

ISBN: (纸本)9781467394727

The proceedings contain 40 papers. The topics discussed include: an effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy;control strategies for intelligent adjustment of pressure in intermittent pneumatic compression systems;protein fold identification using machine learning methods on contact maps;compressed sensing denoising for segmentation of localization microscopy data;computational prediction of bacterial type iv-b effectors using c-terminal signals and machine learning algorithms;a cross-entropy method for change-point detection in four-letter DNA sequences;gene selection using interaction information for microarray-based cancer classification;and computational intelligence for metabolic pathway design: application to the pentose phosphate pathway.

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11th Asian conference on Intelligent Information and Database systems, ACIIDS 2019

11th Asian Conference on Intelligent Information and Databas...

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11th Asian conference on Intelligent Information and Database systems, ACIIDS 2019

ISBN: (纸本)9783030148010

The proceedings contain 124 papers. The special focus in this conference is on Intelligent Information and Database systems. The topics include: Content-Based Music Classification by Advanced Features and Progressive Learning;appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation;a Temporal Approach for Air Quality Forecast;quad-Partitioning-Based Robotic Arm Guidance Based on Image Data Processing with Single Inexpensive Camera For Precisely Picking Bean Defects in Coffee Industry;on the Analysis of Kelly Criterion and Its Application;single Image Super-Resolution with Vision Loss Function;content-Based Motorcycle Counting for Traffic Management by Image Recognition;use of Blockchain in Education: A Systematic Literature Review;a Comparative Study of Techniques for Avoiding Premature Convergence in Harmony Search Algorithm;Analysis of Different Approaches to Designing the Parallel Harmony Search Algorithm for ATSP;an Independence Measure for Expert Collections Based on Social Media Profiles;differential Evolution in Agent-Based Computing;Verifying Usefulness of Ant Colony Community for Solving Dynamic TSP;physical Layer Security Cognitive Decode-and-Forward Relay Beamforming Network with Multiple Eavesdroppers;co-exploring a Search Space in a Group Recommender System;differential Cryptanalysis of Symmetric Block Ciphers Using Memetic Algorithms;modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering;modeling and Features Extraction of Heel Bone Fracture Reparation Dynamical Process from X-Ray Images Based on Time Iteration Segmentation Model Driven by Gaussian Energy;A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization.

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computational Identification of Metabolites for Pathways Related to Huntington's Disease

Computational Identification of Metabolites for Pathways Rel...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Christiana C. Christodoulou George Minadakis Christiana Demetriou Eleni Zamba-Papanicolaou George Spyrou Neurology Clinic D & Bioinformatics Group Cyprus Institute of Neurology and Genetics & Cyprus School of Molecular Medicine Cyprus Bioinformatics Group Cyprus Institute of Neurology and Genetics & Cyprus School of Molecular Medicine Cyprus Cyprus School of Molecular Medicine University of Nicosia Medical School Cyprus Cyprus School of Molecular Medicine Cyprus Institute of Neurology and Genetics Cyprus

Huntington's disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. systems bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HD-related pathways, which were previously identified from our previous work on multi-source data integration These include: i) creation of pathway-to-pathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathway-to-pathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.

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Proceedings - 2017 ieee International conference on bioinformatics and Biomedicine, BIBM 2017

Proceedings - 2017 IEEE International Conference on Bioinfor...

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2017 ieee International conference on bioinformatics and Biomedicine, BIBM 2017

ISBN: (纸本)9781509030491

The proceedings contain 421 papers. The topics discussed include: quantification of visual diagnostic heuristics during simulated pathology diagnosis;on the robustness of mixture model-based unsupervised learning in single-cell analyses;an adaptive refinement for community detection methods for disease module identification in biological networks using novel metric based on connectivity, conductance & modularity;improving quality of ligand-binding site prediction with bayesian optimization;application of deep learning in genomic selection;development of 'KBCommons' - universal informatics framework for multi-omics translational research;near-light perspective shape from shading for 3D visualizations in endoscopy systems;development of an informatics analytics workflow for DAPSEQ data exploration and validation for auxin response factors in maize;discovering multifactorial associations with the development of age-related cataract using contrast mining;a deep-learning framework for amidation site prediction;schema matching and data integration on protein ncounter data;inductive identification of functional status information and establishing a gold standard corpus;and identification of cancer drug sensitivity biomarkers.

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Genome wide computational identification of regulatory RNAs in rice plant pathogen Xanthomonas oryzae

Genome wide computational identification of regulatory RNAs ...

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International conference on bioinformatics and systems Biology (BSB)

作者： Kumari, Priyanka Som, Anup Univ Allahabad Ctr Bioinformat Inst Interdisciplinary Studies Allahabad 211002 Uttar Pradesh India

ISBN: (纸本)9781538664346

Xanthomonas oryzae is a gram negative bacterial phytopathogen which causes leaf blight disease of rice plant, posing serious threat to the plants and their yield. Regulatory RNAs are heterogeneous functional RNA molecules which modulate/regulate gene expression, thus controlling a wide variety of physiological functions. As an effort to understand the role of regulatory RNAs in the physiology and pathogenesis of Xanthomonas oryzae, we identified candidate regulatory RNAs, such as riboswitches (Cobalamin, FMN, Glycine, SAM, TPP, crcB), small RNAs (asX1, asX3, asX4, sX2, sX5, sX6, sX7, sX9, sX11, sX12, sX13, sX14, Xoo1, Xoo2, Xoo5, Xoo8) and ribozyme (RNaseP_arch), using in silico approach. We also identified the genes regulated by these regulatory RNAs and their role.

关键词： Xanthomonas oryzae Regulatory RNAs:(II.) Ribozymes Riboswitches sRNA phytopathogen bioinformatics

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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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A BCHC genetic algorithm model of cotemporal hierarchical Arabidopsis thaliana gene interactions

A BCHC genetic algorithm model of cotemporal hierarchical <i...

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ieee International conference on bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： LaPointe, Bree Ann John, David J. Norris, James L. Allen, Edward E. Harkey, Alexandria F. Muhlemann, Joelle K. Muday, Gloria Qualtrix Salt Lake City UT USA Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA Wake Forest Univ Winston Salem NC 27109 USA

ISBN: (纸本)9781538654880

Gene interaction network models from time course gene transcript abundance data are algorithmically created using a new aggressive genetic algorithm denoted by BCHC. The BCHC algorithm rigorously integrates probabilistic hierarchical likelihood and Bayesian methodology to produce accurate posterior probabilities of interactions between genes after observance of hierarchical gene transcript abundance data. Forbidden pairwise gene relationships are incorporated into the modeling process. This gene interaction model is compared to a previous gene interaction model utilizing the same data and Bayesian likelihood, however based upon an exponentially slower, less aggressive, and less adaptive Metropolis-Hasting search algorithm. In addition for a smaller data set, our gene interaction model is compared to less rigorous non-probabilistic Lasso estimated partial correlation models which do not fully incorporate the hierarchical structure. A comparison is also made between the smallest Bayesian model and tests for edges based on a restricted non-Bayesian hierarchical technique. The BCHC algorithm performs well when the number of genes is moderately increased, both in terms of execution time and model quality.

关键词： bioinformatics Biological system modeling computational systems biology Genetic algorithms Probability Bayes methods

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