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ieee International conference on systems, Man and Cybernetics

作者： Rich Colbaugh Kristin Glass Volv Global Lausanne Switzerland

Countering the substantial threat posed by antimicrobial resistance (AMR) requires the capability to predict pathogen resistance phenotype quickly and accurately. Deriving computational models to form such predictions is challenging for several reasons, perhaps the most daunting being the limited number of reliably-labeled examples from which to learn. We overcome this obstacle with a novel lightly-supervised algorithm that leverages unlabeled and/or noisily-labeled examples - which are often more readily-available - to facilitate model induction. Importantly, we prove the algorithm is safe: adding unlabeled/unreliably-labeled data to the learning procedure produces models which are usually more accurate, and never less accurate, than models learned with reliably-labeled data alone. Experiments demonstrate that the proposed method outperforms state-of-the-art models for the task of predicting AMR using pathogen genome sequences.

关键词： bioinformatics Genomics Pathogens Predictive models Immune system Prediction algorithms Data models

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A multi-tenant computational platform for translational medicine 38

A multi-tenant computational platform for translational medi...

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38th ieee International conference on Distributed Computing systems (ICDCS)

作者： Oehmichen, Axel Guitton, Florian Agapow, Paul Emam, Ibrahim Guo, Yike Imperial Coll London Data Sci Inst London SW7 2AZ England

ISBN: (纸本)9781538668719

Translational biomedical research has become a science driven by big data. Improving patient care by developing personalized therapies and new drugs depends increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of largescale internal and external, partner and public, data sources. As analysing these large-scale and complex datasets has become increasingly computationally expensive, it is of paramount importance to enable researchers to seamlessly scale up their computation platform while being able to manage complex yet flexible scenario that biomedical scientists are asking for. We developed a new platform as an answer to those needs of analysing and exploring massive amounts of medical data with the constrain of enabling the broadest audience, ranging from the medical doctor to the advanced coders, to easily and intuitively exploit this new resource. The platform consists of three main components: Borderline UI, the eTRIKS Analytical Environment (eAE)(1) [I] and the eTRIKS Data Platform (eDP). Each component has been designed to operate on specific sets of problems statelessly, developed as independent open-source software packages, and connected loosely in a cloud-native fashion [2] to form a coherent platform for large scale medical data analysis. The code is fully open source and available on GitHub at this address: https://***/dsi-icl

关键词： Drugs Computer architecture Big Data Biomedical imaging bioinformatics Respiratory system Data analysis

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SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest

SeqScreen: a biocuration platform for robust taxonomic and b...

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ieee International conference on bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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OntoKeeper: Semiotic-driven Ontology Evaluation Tool for Biomedical Ontologists

OntoKeeper: Semiotic-driven Ontology Evaluation Tool for Bio...

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2018 ieee International conference on bioinformatics and Biomedicine, BIBM 2018

作者： Ltuanr Amith, Muhammad Manion, Frank Liang, Chen Harris, Marcelline Wang, Dennis He, Yongqun Tao, Cui School of Biomedical Informatics University of Texas Health Science Center HoustonTX United States Department of Systems Populations and Leadership University of Michigan School of Nursing Ann ArborMI United States Health Informatics Info. Management Lousiana Tech University RustonLA United States University of Texas AustinTX United States Center for Computational Medicine Bioinformatics University of Michigan Medical School Ann ArborMI United States

ISBN: (纸本)9781538654880

Evaluation for ontologies is an important phase in ontology engineering. When evaluating ontologies the designer needs to assess validation and verification aspects of the knowledge artifact. Yet, this task is often disregarded, and therefore, tools that accommodate the workflow of the biomedical ontologists are needed. In this paper. we introduce OntoKeeper, a tool we developed that utilizes a semiotic-driven metric proposed by Burton-Jones and colleagues. Overall, OntoKeeper is the first of a few ontology evaluation tools that can help provide ontology evaluation functionality for knowledge engineers. © 2018 ieee.

关键词： Knowledge management

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Cross-topic Rumour Detection in the Health Domain

Cross-topic Rumour Detection in the Health Domain

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2018 ieee International conference on bioinformatics and Biomedicine, BIBM 2018

作者： Sicilia, Rosa Merone, Mario Valenti, Roberto Cordelli, Ermanno Drantoni, Federico Ruvo, Vincenzo De Dragone, Patrizia Benedetta Esposito, Sara Soda, Paolo Unit of Computer Systems and Bioinformatics Universitá Campus Bio-Medico di Roma Via Alvaro Del Portillo 21 Rome00128 Italy

ISBN: (纸本)9781538654880

Nowadays information diffusion has become more and more immediate and fast thanks to social media and its services. However, lack of controls and moderation in resources as social microblogs often leads to spread unverified information, such as rumours, which can become a threat to the society. To improve life quality and good information diffusion, various automatic systems have been studied for rumour detection in microblogs at level of aggregation of posts, whereas a few effort has been tried to the most challenging scenario where the rumour has to be recognized at level of each single post. In this work, we direct our efforts towards individual post rumour detection: we investigate how features describing influence potential, personal interest and network characteristic perform on two different datasets of posts collected from Twitter using two different health-related keywords. As a further contribution, we study what happens in cross-topic tests, i.e. when the rumour detection system is trained with posts with an hashtag and tested on samples with a different one. © 2018 ieee.

关键词： Health

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Radiomics for Predicting CyberKnife response in acoustic neuroma: A pilot study

Radiomics for Predicting CyberKnife response in acoustic neu...

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2018 ieee International conference on bioinformatics and Biomedicine, BIBM 2018

作者： Dramico, Natascha Claudia Sicilia, Rosa Cordelli, Ermanno Valbusa, Giovanni Grossi, Enzo Zanetti, Isa Bossi Beltramo, Giancarlo Fazzini, Deborah Scotti, Giuseppe Iannello, Giulio Soda, Paolo Imaging Department Centro Diagnostico Italiano S.p.A. Milan Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Italy Joint Laboratory on Precision Medicine and BioData Analytics Università Campus Bio-Medico di Roma Centro Diagnostico Italiano S.p.A. Milan Italy Cyberknife Department Centro Diagnostico Italiano S.p.A. Milan Italy Bracco Imaging S.p.A. Milan Italy

ISBN: (纸本)9781538654880

Vestibular schwannomas, also known as acoustic neuromas, are a primary intracranial tumor of the myelin-forming cells of the 8th cranial nerve. Stereotactic radiosurgery, which can be performed with the CyberKnife robotic device, is one of the approaches for managing this disease, and has shown to be effective in controlling tumor growth. However, it may have side effects and up to two years may be needed to assess its efficacy. In this work we present a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images routinely collected before the CyberKnife treatment and then predicts the treatment response. Furthermore, the degree of class imbalance observed in the available dataset suggested us to resample the data during the learning stage. The results achieved are promising, with an accuracy equal to 85.3%. © 2018 ieee.

关键词： Machine learning

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Modeling of Glutamate Pathway in Alzheimer's Disease using Biochemical systems Theory 7

Modeling of Glutamate Pathway in Alzheimer's Disease using B...

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7th International conference on Computing, Communications and Informatics (ICACCI)

作者： Sasidharakurup, Hemalatha Pradeep, Malavika Bhaskaran, Maanasa Priya, Anu Pradeep, Evelin Kadavath, Shruthi Nair, Bipin Diwakar, Shyam Amrita Vishwa Vidyapeetham Amrita Sch Biotechnol Amritapuri CampusClappana PO Kollam 690525 India

ISBN: (纸本)9781538653142

Alzheimer's disease is an age-related, progressive and chronic neurodegenerative disorder caused by neuronal cell death and attributed to memory loss. Recent studies have shown that glutamate has an important role in learning, memory formation and synaptic plasticity. A disruption to glutamatergic neurotransmission has been suggested to lead to Alzheimer's Dementia. As a main objective, this study computational models the glutamate pathway to relate molecular mechanisms of how glutamate links to neural activity and predict the involvement in Alzheimer's disease. Simulations was by numerical solving ordinary differential equations as rate equations based on biochemical systems theory. Results suggest in normal or control condition there was an increase in astrocyte calcium levels. Our simulations showed a lower level of GluN2B level in control suggesting a reduced production of amyloid beta compared to the diseased condition. Simulating diseased state, increased IP3 levels lead to the release of TNF alpha, released glutamine synthase that converted glutamine to glutamate. The results also demonstrated the elevation in extracellular glutamate concentration that activated NMDAR. Through modeling, we show that increased production of amyloid beta that forms amyloid plaques elucidating the main component of neurodegeneration occurred in Alzheimer's disease.

关键词： Alzheimer's disease biochemical systems theory cell death mathematical modeling bioinformatics

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Guest Editorial ieee International conference on bioinformatics and Biomedicine (BIBM) 2015 Special Issue

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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ieee TRANSACTIONS ON NANOBIOSCIENCE 2016年第4期15卷 306-308页

作者： Hu, Xiaohua Huang, Yufei Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 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 (BIBM) 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 Genomics computational systems biology Biomedical informatics

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GHOSTZ PW/GF: Distributed Parallel Homology Search System for Large-scale Metagenomic Analysis

GHOSTZ PW/GF: Distributed Parallel Homology Search System fo...

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ieee International conference on Big Data

作者： Kenta Machida Osamu Tatebe Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Genomic data, a typical example of big data, is increasing annually owing to the extensive usage and improved performance of next-generation sequencers. Homology search, which is used for genome analysis, is a technique in which homologous DNA from a known DNA sequence database is collected using unknown DNA sequences as a query. However, the conventional method suffers from performance issues and memory shortage. In this study, we proposed and implemented a distributed parallel homology search system GHOSTZ PW/GF using Gfarm, a distributed file system, and Pwrake, a dynamic workflow engine and evaluated them in TSUBAME3.0. The results indicate the high scalability of the proposed system; additionally, using a prebuilt non-redundant database comprising approximately 100 million records and sequence data comprising approximately 500 million records, the proposed system completed the execution of all processes on 180 nodes in approximately 2 h. To the best of the authors' knowledge, this is the first large-scale metagenomic analysis attempted using such a large number of queries and DBs.

关键词： Task analysis Genomics bioinformatics Microorganisms Tools Supercomputers DNA

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MESGA: An MPSoC Based Embedded System Solution for Short Read Genome Alignment 18

MESGA: An MPSoC Based Embedded System Solution for Short Rea...

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23rd Asia and South Pacific Design Automation conference (ASP-DAC)

作者： Gnanasambandapillai, Vikkitharan Bayat, Arash Parameswaran, Sri Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

ISBN: (纸本)9781509006021

computational needs for genome processing are often satiated by enormous servers or by cloud computers. Even though there has been some work in implementing some aspects of genome processing in GPUs and FPGAs, they are often accelerators for such servers and not stand-alone systems. In this paper, for the first time, we present a method to entirely move the alignment process to embedded processors. Such a system is useful in a variety of situations where significant networked infrastructure is not available, and where privacy is a concern. A ring pipelined processor architecture for short read alignment, based on partitioned genome references is shown. A timeout based alignment method is proposed to prevent unnecessary exhaustive search. The proposed partitioning method allows an entire human genome to be processed using small embedded processors. Experimental results show that the proposed solution speeds up the performance by approximately seven times with 16 embedded processors when compared to a linear pipelined system. It is expected that the proposed solution will to lead to a portable genome analyzing device, significantly reducing cost and testing time.

关键词： Genomics bioinformatics Program processors Field programmable gate arrays Indexes Memory management

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