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Behavior and Depression Gene Networks Reconstruction and Analysis

Behavior and Depression Gene Networks Reconstruction and Ana...

ieee Region 8 International conference on computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： R.A. Ivanov V.I. Zamyatin A.I. Klimenko T.N. Astakhova Y.G. Matushkin A.N. Savostyanov S.A. Lashin Sector for bioinformatics and information technologies in genetics Institute of Cytology and Genetics SB RAS Novosibirsk State University Novosibirsk Russian Federation Laboratory of Biological Markers of Human Social Behavior Humanitarian Institute Novosibirsk State University Novosibirsk Russian Federation Laboratory of Molecular-Genetic Systems Institute of Cytology and Genetics Novosibirsk Russian Federation Laboratory of Psychological Genetics Institute of Cytology and Genetics Laboratory of Differential Psychophysiology Institute of Physiology and Basic Medicine Novosibirsk Russian Federation

One of the main interests of modern neurophysiology is aimed at studying biology bases of anxiety and depression. The unsolved problem is complex integration between heredity factors, indexes of the brain electrical activity and psychological traits of people. Neurobiological understanding of this topic is essential not only for identification of genes involved in molecular mechanisms of depression, but also for knowledge of complex relationship of these genes. Research of these interactions could contribute to development of new prevention, diagnosis and treatment methods of depression. In our research, we reconstruct several PPI networks and networks of transcriptional regulation as well, in order to build the model of complex interaction of environmental and heredity factors of vulnerability to depression and other mental disorders. We identify the number of genes, which expression is supposedly related to environmental factors, for further experimental verification.

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Doppio: I/O-Aware Performance Analysis, Modeling and Optimization for In-Memory Computing Framework

Doppio: I/O-Aware Performance Analysis, Modeling and Optimiz...

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ieee International Symposium on Performance Analysis of systems and Software (ISPASS)

作者： Zhou, Peipei Ruan, Zhenyuan Fang, Zhenman Shand, Megan Roazen, David Cong, Jason Univ Calif Los Angeles Los Angeles CA 90024 USA Broad Inst Cambridge MA USA Xilinx San Jose CA USA

ISBN: (纸本)9781538650103

In conventional Hadoop MapReduce applications, I/O used to play a heavy role in the overall system performance. More recently, a study from the Apache Spark community-state-of-the-art in-memory cluster computing framework-reports that I/O is no longer the bottleneck and has a marginal performance impact on applications like SQL processing. However, we observe that simply replacing HDDs with SSDs in a Spark cluster can have over 10x performance improvement for certain stages in large-scale production-quality genome processing. Therefore, one key question arises: How does I/O quantitatively impact the performance of today's big data applications developed using in-memory cluster computing frameworks like Apache Spark? In this paper we select an important yet complex application-the Spark-based Genome Analysis ToolKit (GATK4)-to guide our modeling. We first use different combinations of HDDs and SSDs to measure the I/O impact on GATK4 and change the CPU core number to discover the relation between computation and I/O access. By combining with Spark's underlying implementations, we further analyze the inherent cause of the above observations and build our model based on the analysis. Although we are building upon GATK4, our model maintains generality to other applications. Experimental results show that we can achieve a performance prediction error rate within 10% for typical Spark applications of both iterative and shuffle-heavy algorithms. Finally, we further extend our model to a broader area-that of optimal configuration selection in the public cloud. In Google Cloud, our model enables us to save 38% to 57% of cost for genome sequencing compared with its recommended default configurations. Currently, more and more companies are adopting cloud computing for specific workloads. Our proposed model can have a huge impact on their choices, while also enabling them to significantly reduce their costs.

关键词： Sparks Genomics bioinformatics computational modeling Analytical models Cloud computing

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Identification of intrinsic disorder in complexes from Protein Data Bank

Identification of intrinsic disorder in complexes from Prote...

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

作者： Jianhong Zhou Christopher J. Oldfield Fei Huang Wenying Yan Bairong Shen A. Keith Dunker Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN United States Computer Science Department Virginia Commonwealth University Richmond VA United States Institute for Systems Genetics West China Hospital Sichuan University Chengdu Sichuan China

ISBN: (纸本)9781538685211

Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of the sequence → structure → function paradigm. In this study, we first present the results of various disorder predictions on a non-redundant set of PDB complexes. Further, structural formation by these predicted-to-be-disordered proteins was observed to depend upon various factors (including hetero group binding, protein/DNA/RNA binding, disulfide bonds, and ion binding). This study provides additional support for the newly proposed paradigm of sequence → IDP/IDR ensemble → function.

关键词： Proteins computational biology bioinformatics systems biology Computer science Genetics Hospitals

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From gene panels to systems medicine

From gene panels to systems medicine

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Jan Baumbach Department of Automation Shanghai Key Laboratory of Navigation and Location Based Services Shanghai China

ISBN: (纸本)9781538654897;9781538654880

On major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. Here, we will discuss the diagnostic and prognostic value of (multi-)omics panels in general. We will have a closer look at breast cancer subtyping and treatment outcome, as case example, using gene expression panels - and we will discuss the current “best practice” in the light of critical statistical considerations. Afterwards, we will introduce computational approaches for network-based medicine aka. systems medicine. We will discuss novel developments in graph-based machine learning using examples ranging from Huntington's disease mechanisms via lung cancer drug target discovery back to where we started, i.e. breast cancer subtyping and treatment optimization - but now from a systems medicine point of view. We conclude that systems medicine and modern artificial intelligence open new avenues to shape future medicine.

关键词： bioinformatics Breast cancer Head Magnetic heads conferences Drugs

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Using autoencoders and text mining to characterize single cell populations in the hippocampus and cortex

Using autoencoders and text mining to characterize single ce...

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2018 systems and Information Engineering Design Symposium, SIEDS 2018

作者： Wall, Morgan Dreisbach, Caitlin Zaidi, Ali Flower, Abigail Overall, Chris Candidate Data Science Institute University of Virginia United States Data Science Institute School of Nursing University of Virginia United States Data Science Institute University of Virginia United States Systems and Information Engineering University of Virginia United States University of Virginia United States

ISBN: (纸本)9781538663431

Revolutionary advances in genomic technology have allowed researchers to address biological questions about cell types, states, and gene regulation at the scale of single cells. However, the ability to characterize gene expression and function of individual cells brings with it new data-related challenges, such as dimensionality, feature reduction, and noise reduction. The central objective of this research was to use existing methods in a novel application of single-cell gene expression data to better characterize sub-populations of cell types in various regions of the brain. This research approach used a computational bioinformatics pipeline for single-cell RNA-sequencing (RNA-seq) normalization and clustering. Data science methodologies, such as autoencoding and text mining, were adapted to identify candidate gene sets that distinguish different types of cells in the central nervous system. Then, the functional themes of these gene sets were inferred using a combination of functional enrichment of gene ontology terms and topic modeling. Topic modeling revealed various functional themes among the clusters, in some cases reinforcing the results of biomarker analysis, and in other cases providing further insight into potential functional differences between clusters. For one cluster in the cortex, an immune theme emerged with stemmed-words as specific as 'immun' and 'antigen' appearing in the results. In the hippocampus, clusters determined to be neurons could be further differentiated as themes related to various organs were identified. One of these clusters featured a vascular theme with words related to 'endotheli.' Future applications of these methods intend to expound upon specific cellular processes, in relation to immune function, and translational research on neurological disease states. © 2018 ieee.

关键词： Genome

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16th International conference on Practical Applications of Agents, Multi-Agent systems, PAAMS 2018

16th International Conference on Practical Applications of A...

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16th International conference on Practical Applications of Agents, Multi-Agent systems, PAAMS 2018

ISBN: (纸本)9783319945798

The proceedings contain 88 papers. The special focus in this conference is on Practical Applications of Agents. The topics include: Optimizing opponents selection in bilateral contracts negotiation with particle swarm;reputation computational model to support electricity market players energy contracts negotiation;script language to describe Agent’s behaviors;multi-agent systems approach to industry 4.0: Enabling collaboration considering a blockchain for knowledge representation;agents in logistics and supply chain;Automatic verification of design rules in PCB manufacturing;ABIBA: An agent-based computing system for behaviour analysis used in human-agent interaction;using crowdsourcing for the development of online emotional support agents;on “influencers” and their impact on the diffusion of digital platforms;scheduling of home health care services based on multi-agent systems;household occupancy detection based on electric energy consumption;information fusion and machine learning in spatial prediction for local agricultural markets;SMEC-3D: A multi-agent 3D game to cognitive stimulation;how machine learning could detect anomalies on *** platform?;AIDE-VR: Extending a virtual living lab framework using virtual reality;multi agent system application for electrical load shedding management: Experiment in Senegal power grid;a fuzzy-based multi-agent model to control the micro-grid operation based on energy market dynamics;coalitions of end-use customers in retail electricity markets: A real-world case study involving five schools for children;distributed multi-agent based energy management of smart micro-grids: Autonomous participation of agents in power imbalance handling;design of an agent-based learning environment for high-risk doorstep scam victims;significant educational content based learning model using public ontologies and multiagent systems.

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A Smart Sensor Architecture for eHealth Applications

A Smart Sensor Architecture for eHealth Applications

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ieee Annual International Computer Software and Applications conference (COMPSAC)

作者： Ermanno Cordelli Giorgio Pennazza Marco Sabatini Marco Santonico Luca Vollero Computational Systems and Bioinformatics Lab Università Campus Bio-Medico di Roma Electronics for Sensor Systems Lab Università Campus Bio-Medico di Roma

The diffusion of cheap sensing and programmable hardware systems enables today the quick design of complex smart sensor devices, i.e. sensing systems equipped with computational and communication functionalities, ready to be included in IoT systems. In this paper we present a general software architecture for the implementation of such smart sensors. The goal of the proposed model is to provide a reference framework usable in multiple scenarios, suitable for the management of different sensors, and providing a general and standard interface and simple computational functionalities. The paper presents our model, examples of different smart sensors based on our model and the implementation, test and performance assessment of a simple analogue smart sensor. The implementation of the proposed model is available online as a template for the design and development of smart sensors equipped with updatable edge computing functionalities. The performance evaluation of our analogue smart sensor shows that the application of our model has limited overhead which is highly compensated by its flexibility.

关键词： Micromechanical devices Intelligent sensors Computer architecture computational modeling Transducers Microcontrollers

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Data Provenance Management for bioinformatics Workflows using NoSQL Database systems in a Cloud Computing Environment

Data Provenance Management for Bioinformatics Workflows usin...

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

作者： Hondo, Fernanda Wercelens, Polyane da Silva, Waldeyr Castro, Klayton Santana, Ingrid Walter, Maria Emilia Araujo, Aleteia Holanda, Maristela Lifschitz, Sergio Univ Brasilia Dept Comp Sci Brasilia DF Brazil Pontificia Catolica Univ Rio de Janeiro Informat Dept Rio De Janeiro Brazil

ISBN: (纸本)9781509030507

Computer science solutions for molecular biology problems are often presented in the form of workflows. There is a set of activities performed by different processing entities through managed tasks. Knowledge about the data trajectory throughout a given workflow enables reproducibility by data provenance. In order to reproduce an in silico bioinformatics experiment one must consider other aspects besides those steps followed by a workflow. Indeed, the computational settings in which the involved programs run is a requirement for reproducibility. Cloud computing technology may hide the technical details and make it easier for the user to set up such an ondemand environment. NoSQL database systems have also gained popularity, particularly in the cloud. Considering this particular scenario, we have planned and executed a research study about a bioinformatics workflow running in an IaaS cloud computing environment. We have persisted provenance data according to the PROV-DM model, using different types of NoSQL database systems. We present in this paper some preliminary results from our reseach work, where we have explored the characteristics of several NoSQL database systems to persist provenance data.

关键词： bioinformatics workflows cloud computing NoSQL data provenance

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Guest Editorial for Special Section on the 11th International conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational BIOLOGY AND bioinformatics 2017年第5期14卷 1104-1105页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai Peoples R China Polytech Bari Elect & Informat Engn Dept I-70126 Bari Italy Indian Inst Technol Dept Biotechnol Madras 600036 Tamil Nadu India

The papers in this special section were presented at the 11th International conference on Intelligent Computing ICIC held in Fuzhou, China, on August 20-23, 2015. This conference was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Artificial intelligence Machine learning bioinformatics Biomedical engineering Predictive models Intelligent systems

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Convolutional Neural Network Approach to Lung Cancer Classification Integrating Protein Interaction Network and Gene Expression Profiles

Convolutional Neural Network Approach to Lung Cancer Classif...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Teppei Matsubara Jose C. Nacher Tomoshiro Ochiai Morihiro Hayashida Tatsuya Akutsu Dept. Information Science Faculty of Science Toho University Funabashi Chiba Japan Toho Daigaku Ota-ku Tokyo JP Department of Social Information Studies Otsuma Women's University Tokyo Japan Dept. of Electrical Engineering and Computer Science Matsue College Shimane Japan

ISBN: (纸本)9781538650431

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks to 'omics' data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a convolutional neural network (CNN) approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. Data and CNN code can be downloaded from the link: https://***/site/nacherlab/analysis.

关键词： Proteins Gene expression Two dimensional displays Convolutional neural networks Cancer bioinformatics Lung

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