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ieee TRANSACTIONS ON NANOBIOSCIENCE 2017年第5期16卷 312-313页

作者： Hu, Xiaohua Tony Drexel Univ Philadelphia PA 19104 USA Nortel Res Ctr Brampton ON Canada Verizon Lab Waltham MA USA DMW Software Silicon Valley CA USA Coll Comp & Informat Data Min & Bioinformat Lab Philadelphia PA 19104 USA NSF Ctr I U CRC Visual & Decis Informat NSF CVDI Philadelphia PA 19104 USA

bioinformatics and biomedicine research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. This type of research requires close collaboration among multidisciplinary teams of researchers in computer science, statistics, physics, engineering, life sciences and medical sciences, and their interfaces. The ieee International conference on bioinformatics and Biomedicine aims to provide an open and interactive forum to catalyze the cross-fertilization of ideas from these disciplines and to bridge our knowledge gaps.

关键词： Special issues and sections Meetings bioinformatics computational systems biology Drugs

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Predictive Deep Network with Leveraging Clinical Measure as Auxiliary Task

Predictive Deep Network with Leveraging Clinical Measure as ...

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Biological Ontologies and Knowledge Bases Workshop at ieee International conference on bioinformatics and Biomedicine (ieee BIBM)

作者： Li, Xiangrui Zhu, Dongxiao Levy, Phillip Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Wayne State Univ Dept Emergency Med Detroit MI USA Wayne State Univ Cardiovasc Res Inst Detroit MI USA

ISBN: (纸本)9781509030507

Deep neural networks (DNNs) have made impressive improvements for predictive modeling in various fields. Successful network building for predictive tasks usually requires abundant data, for effectively learning high-level, non-additive information from raw input features. The merit of high-level learning makes DNN promising in clinical research, but the need for abundant data hampers DNN applications. To address this challenge, we propose the use of Auxiliary-Task-Augmented Network (ATAN), a predictive model (for primary target) with introducing auxiliary tasks as regularization. ATAN leverages clinically relevant measures as auxiliary targets and learns the clinical relevance explicitly. We apply ATAN in a clinical dataset of hypertension collected from a vulnerable demographic population (African-American) to demonstrate its effectiveness.

关键词： Predictive models Hypertension computational modeling Heart bioinformatics Electronic mail

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endo-siRBase: A multi-species developmental endo-siRNA repository and searchable database

endo-siRBase: A multi-species developmental endo-siRNA repos...

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ieee International conference on computational Advances in Bio and Medical Sciences (ICCABS)

作者： Kelly Daescu Biological Sciences University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781538685211

Endogenous short interfering RNAs (endo-siRNAs) play an important role in gene regulation in many different model systems, yet to our knowledge there is no database that contains information for those that are experimentally validated. The endo-siRbase platform reported in this paper fills this void by housing endo-siRNA fasta files from several species. These files were generated from reads derived from small RNA datasets that did not map to miRNA, piRNA, or any other non-coding RNA but that did map antisense to annotated mRNA. This repository of endo-siRNAs is groundbreaking because it is the first to provide an official annotation while also citing the endo-siRNA experimental source and any available developmental information, such as stage or regional expression. In addition to providing validated endo-siRNA sequence information, endo-siRbase also provides a search tool that reports potential mRNA targets of an endo-siRNA sequence as well as any documented endo-siRNAs that are antisense to a specified mRNA. Due to the exponential growth of small RNA datasets and the mounting evidence for the critical role endo-siRNAs play in development, this database and search tool will benefit developmental biologists not only by providing an official annotation and a uniform identification procedure, but also by consolidating datasets into one location.

关键词： RNA Libraries Databases Tools Genomics bioinformatics

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2017 ieee International conference on bioinformatics and Biomedicine, BIBM 2017

作者： Sicilia, Rosa Giudice, Stella Lo Pei, Yulong Pechenizkiy, Mykola Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy 2M Engineering Ltd Valkenswaard Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600 MB Netherlands

ISBN: (纸本)9781509030491

In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision. © 2017 ieee.

关键词： Health

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Quantifying and Mitigating computational Inefficiency of Genomics Data Analysis 19

Quantifying and Mitigating Computational Inefficiency of Gen...

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19th ieee International conference on High Performance Computing and Communications (HPCC) / 15th ieee International conference on Smart City (SmartCity) / 3rd ieee International conference on Data Science and systems (DSS)

作者： Li, Xueqi Tan, Guangming Zhang, Chunming Li, Xu Zhang, Zhonghai Sun, Ninghui Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538625880

In this paper, we performed a comprehensive study of quantifying and mitigating computational inefficiency of current genomic analysis approaches. First, we found current parallelization approaches that have limited scalability due to either unexploited parallelism or low utilization of system resource. Thus, we proposed Spark-Gene, which is on the basis of Spark in-memory programming model. To test the performance of our Spark-Gene, we used WGS in the GATK as the test case. We show that Spark-Gene reduces the execution time of WGS analysis from 19 hours to 30 minutes with a speedup in excess of 37-fold at 256 CPU cores. Furthermore, we identified that garbage collection is the major scalable bottleneck of better parallel efficiency for native in-memory computing model. Second, we quantified microarchitectural inefficiency for typical genomic applications and uncovered opportunities for microarchitectural optimizations for the design of genomic domain-specific accelerator, especially on specializing concurrency, computation and memory hierarchy. This paper is to leverage state-of-art big-data technologies to improve parallelization for genomics analysis and motivate the integration of accelerators into the genomic analysis computing system.

关键词： Genomics bioinformatics Pipelines Sequential analysis Microarchitecture Cancer Tools

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Identifying Articles Relevant to Drug-Drug Interaction: Addressing Class Imbalance

Identifying Articles Relevant to Drug-Drug Interaction: Addr...

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Biological Ontologies and Knowledge Bases Workshop at ieee International conference on bioinformatics and Biomedicine (ieee BIBM)

作者： Zhang, Gongbo Bhattacharya, Moumita Wu, Heng-Yi Li, Pengyuan Li, Lang Shatkay, Hagit Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Indiana Univ Sch Informat & Comp Indianapolis IN 46204 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Univ Delaware Ctr Bioinformat & Computat Biol Delaware Biotechnol Inst Newark DE USA Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)9781509030507

Interactions between drugs (also known as drug-drug interactions or DDIs), which may cause adverse affects, are of much concern;predicting, anticipating and avoiding them is key for improving patient safety and treatment outcome. Knowledge of DDIs is important for physicians to avoid adverse effects when prescribing two drugs simultaneously. DDIs are often published in the biomedical literature;however, gathering information about DDIs is time consuming given the shear volume of publications. Automatic text classification can speed up access to documents related to DDIs. However, the biomedical literature contains a relatively small number of publications relevant to DDIs, compared to the vast amount of irrelevant publications. This imbalance can lead to incorrect classification. While methods addressing class imbalance have been introduced to correctly identify items in the minority (relevant) class to improve recall, they often misclassify items in the majority (irrelevant) class, which leads to low precision. To reduce the number of irrelevant documents misclassified as relevant (false positive), we develop a two-stage cascade classifier. In each step, we separate publication abstracts that are DDI-relevant from those that are either drug-irrelevant or drug-relevant but DDI-irrelevant. We compare our classifier with other popular learning methods that aim to handle imbalance, applying the methods to a well-curated corpus consisting of DDI-relevant and DDI-irrelevant PubMed abstracts. Our method achieves higher precision and F1 measure than other methods while maintaining similar recall.

关键词： Drugs Training Dictionaries bioinformatics computational biology Safety

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Comparative Analysis of de Bruijn Graph Parallel Genome Assemblers

Comparative Analysis of de Bruijn Graph Parallel Genome Asse...

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International Work conference on Bio-inspired Intelligence (IWOBI)

作者： Carlos Gamboa-Venegas Esteban Meneses School of Computing Costa Rica Institute of Technology

Finding the genome of new species remains as one of the most crucial tasks in molecular biology. To achieve that end, de novo sequence assembly feeds from the vast amount of data provided by Next-Generation Sequencing technology. Therefore, genome assemblers demand a high amount of computational resources, and parallel implementations of those assemblers are readily available. This paper presents a comparison of three well-known de novo genome assemblers: Velvet, ABySS and SOAPdenovo, all of them using de Bruijn graphs and having a parallel implementation. We based our analysis on parallel execution time, scalability, quality of assembly, and sensitivity to the choice of a critical parameter ( k -mer size). We found one of the tools clearly stands out for providing faster execution time and better quality in the output. Also, all assemblers are mildly sensitive to the choice of k-mer size and they all show limited scalability. We expect the findings of this paper provide a guide to the development of new algorithms and tools for scalable parallel genome sequence assemblers.

关键词： bioinformatics Genomics Program processors Sequential analysis Message systems Scalability Parallel programming

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Classification of Colorectal Cancer Using Clustering and Feature Selection Approaches 1

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11th International conference on Practical Applications of computational Biology and bioinformatics (PACBB)

作者： Nies, Hui Wen Daud, Kauthar Mohd Remli, Muhammad Akmal Mohamad, Mohd Saberi Deris, Safaai Omatu, Sigeru Kasim, Shahreen Sulong, Ghazali Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Locked Bag 01 Kota Baharu 16300 Kelantan Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Malaysia Univ Malaysia Terengganu Sch Informat & Appl Math Kuala Nerus 21030 Terengganu Malaysia

ISBN: (数字)9783319608167

ISBN: (纸本)9783319608167;9783319608150

Accurate cancer classification and responses to treatment are important in clinical cancer research since cancer acts as a family of gene-based diseases. Microarray technology has widely developed to measure gene expression level changes under normal and experimental conditions. Normally, gene expression data are high dimensional and characterized by small sample sizes. Thus, feature selection is needed to find the smallest number of informative genes and improve the classification accuracy and the biological interpretability results. Due to some feature selection methods neglect the interactions among genes, thus, clustering is used to group the similar genes together. Besides, the quality of the selected data can determine the effectiveness of the classifiers. This research proposed clustering and feature selection approaches to classify the gene expression data of colorectal cancer. Subsequently, a feature selection approach based on centroid clustering provide higher classification accuracy compared with other approaches.

关键词： Cancer classification Gene expression data Feature selection Clustering Artificial intelligence bioinformatics

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The emerging field of clinical bioinformatics: Infrastructure, analytical tools, and knowledge bases 4

The emerging field of clinical Bioinformatics: Infrastructur...

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4th International conference on Advances in Biomedical Engineering, ICABME 2017

作者： Abouelhoda, Mohamed Systems and Biomedical Engineering Department Cairo University Giza Egypt Saudi Human Genome Program King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia

ISBN: (纸本)9781538616420

The availability of cost effective and fast genome sequencing techniques has paved the way for a personalized medical practice. The new sequencing technology produces huge datasets with different characteristics and the analysis has evolved from a single program to a workflow of tens of analytical steps, starting from the analysis of raw data until generating clinical reports. The mix between big data, analysis workflows, and clinical requirements has shaped the field of clinical bioinformatics. In this paper, we review the basic components of this field, including computational infrastructure, analytical workflows, and knowledge bases for interpretation of results and for reaching clinical decisions. © 2017 ieee.

关键词： bioinformatics

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computational methods in systems biology: 15th international conference, CMSB 2017 Darmstadt, Germany, september 27–29, 2017 proceedings 15th

Computational methods in systems biology: 15th international...

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15th International conference on computational Methods in systems Biology, CMSB 2017

作者： Feret, Jérôme Koeppl, Heinz Inria & École normale supérieure Paris France Technische Universität Darmstadt Darmstadt Germany

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