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Computational inference of gene regulatory networks: Approaches, limitations and opportunities

BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2017年第1期1860卷 41-52页

作者： Banf, Michael Rhee, Seung Y. Carnegie Inst Sci Dept Plant Biol 260 Panama St Stanford CA 93405 USA

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Computational systems biology Gene regulatory network inference Machine learning heterogeneous data integration Gene network evaluation

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Deep kernel dimensionality reduction for scalable data integration

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2016年 74卷 121-132页

作者： Sokolovska, Nataliya Clement, Karine Zucker, Jean-Daniel Hop La Pitie Salpetriere Assistance Publ Hop Paris ICAN Inst Cardiometab & Nutr Paris France Univ Paris 06 Sorbonne Univ UMR S 1166 NutriOm TeamICAN Paris France INSERM UMR S U1166 NutriOm Team Paris France UMMISCO UMI 209 Inst Dev Res Bondy France

Dimensionality reduction is used to preserve significant properties of data in a low dimensional space. In particular, data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. data integration, however, is a challenge in itself. In this contribution, we consider a general framework to perform dimensionality reduction taking into account that data are heterogeneous. We propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. The method can be also used to learn shared representations between modalities. We show by experiments on standard and on real large-scale biomedical data sets that the proposed method embeds data in a new compact meaningful representation, and leads to a lower classification error compared to the state-of-the-art methods. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Dimensionality reduction heterogeneous data integration

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A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in MATLAB

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EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY 2016年第1期2017卷 1-30页

作者： Sinha, Shriprakash 104 Madhurisha Hts Phase 1 Risali 490006 Bhilai India

Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid biologists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the MATLAB code and causal models from Bayesian network toolbox. The manuscript elucidates the coding contents of the advance article by Sinha (Integr. Biol. 6:1034-1048, 2014) and takes the reader in a step-by-step process of how (a) the collection and the transformation of the available biological information from literature is done, (b) the integration of the heterogeneous data and prior biological knowledge in the network is achieved, (c) the simulation study is designed, (d) the hypothesis regarding a biological phenomena is transformed into computational framework, and (e) results and inferences drawn using d-connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands-on experience of a perturbation project. Description of MATLAB files is made available under GNU GPL v3 license at the Google code project on https://***/p/static-bn-for-wnt-signaling-pathway and https://***/site/shriprakashsinha/shriprakashsinha/projects/static-bn-for-wnt-signaling-pathway. Latest updates can be found in the latter website.

关键词： Wnt signaling pathway Bayesian network Prior biological knowledge Epigenetic information heterogeneous data integration Hypothesis testing Inference

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heterogeneous SENSOR data EXPLORATION AND SUSTAINABLE DECLARATIVE MONITORING ARCHITECTURE: APPLICATION TO SMART BUILDING 1

HETEROGENEOUS SENSOR DATA EXPLORATION AND SUSTAINABLE DECLAR...

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1st International Conference on Smart data and Smart Cities

作者： Servigne, Sylvie Gripay, Yann Pinarer, Ozgun Samuel, John Ozgovde, Atay Jay, Jacques Univ Lyon CNRS INSA Lyon LIRISUMR CNRS 5205 F-69621 Villeurbanne France Galatasaray Univ Dept Comp Engn Ciragan Cad 36 TR-34349 Istanbul Turkey Univ Lyon CNRS INSA Lyon CETHILUMR CNRS 5008 F-69621 Villeurbanne France

Concerning energy consumption and monitoring architectures, our goal is to develop a sustainable declarative monitoring architecture for lower energy consumption taking into account the monitoring system itself. Our second is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon like energy consumption of smart building vs inhabitants' social behaviours. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multidimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand relationship between physical phenomena and user behaviors. The aim of this paper is to give a presentation of our methodology and results concerning architecture and user-centric tools.

关键词： Sensor data model heterogeneous data integration data Cross-Analysis data exploration Monitoring architecture Energy consumption Smart Building

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Mango: combining and analyzing heterogeneous biological networks

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BIOdata MINING 2016年第1期9卷 25-25页

作者： Chang, Jennifer Cho, Hyejin Chou, Hui-Hsien Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50011 USA Iowa State Univ Dept Comp Sci Ames IA 50011 USA

Background: heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data sets requires a more standardized method for integration and exploration. Results: Mango facilitates large network analyses with its Graph Exploration Language, automatic graph attribute handling, and real-time 3-dimensional visualization. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways. Conclusions: Mango is written in C++ and runs on Mac OS, Windows, and Linux. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://***/download/Mango. The Mango User Guide listing all features can be found at http://***/book/j23414/mango-user-guide.

关键词： Systems biology heterogeneous data integration Biological pathway analysis 3D visualization Graph mathematics

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heterogeneous data integration FRAMEWORK BASED ON GRID SERVICE

HETEROGENEOUS DATA INTEGRATION FRAMEWORK BASED ON GRID SERVI...

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IEEE International Conference on Network Infrastructure and Digital Content

作者： Liu, Yanbing Liu, Zhangxiong Luo, Laiming Chongqing Univ Posts & Telecommun Sch Comp Sci Chongqing Peoples R China ChongQing Jugou Technol Dev Co Ltd Chongqing Peoples R China

ISBN: (纸本)9781424448982

heterogeneous data integration plays an important role in the information management system (IMS). Traditional IMS have low integration degree and are difficult to adapt to dynamic changes of databases flexibly. In this paper, we propose a grid service framework for heterogeneous data integration. This framework consists of three layers, including data service layer, data integration layer and data display layer. The data integration layer is the core of the whole framework. An IMS is implemented according to the proposed framework, and massive data are used for the system implementation to verify the correctness of results practically. System performance evaluations are reported to establish the superiority of the grid technique in terms of the round-trip time and the CPU occupancy.

关键词： heterogeneous data integration grid technique IMS grid service

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Continuous and Discrete Deep Classifiers for data integration 14th

Continuous and Discrete Deep Classifiers for Data Integratio...

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14th International Symposium on Intelligent data Analysis (IDA)

作者： Sokolovska, Nataliya Rizkalla, Salwa Clement, Karine Zucker, Jean-Daniel Hop La Pitie Salpetriere Assistance Publ Hop Paris ICAN Paris France UPMC Univ Paris 6 Sorbonne Univ UMR S 1166 ICANNutriOmics Team Paris France NutriOmics Team UMR S U1166 INSERM Paris France UMMISCO UMI 209 Inst Dev Res Bondy France

ISBN: (纸本)9783319244655

data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. A final compact model has to be interpreted by human experts, and interpretation of a classifier whose weights are discrete is much more straightforward. In this contribution, we propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. We show by experiments on standard and on real large-scale biomedical data sets that the proposed method embeds data in a new compact meaningful representation, and leads to a lower classification error compared to the state-of-the-art methods. We also consider some state-of-the art deep learners and their corresponding discrete classifiers. We illustrate by our experiments that although purely discrete models do not always perform better than real-valued classifiers, the trade-off between the model accuracy and the interpretability is quite reasonable.

关键词： Dimensionality reduction heterogeneous data integration Bioinformatics

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Research on heterogeneous data integration in the Safety Production and Management of Coal-mining

Research on Heterogeneous Data Integration in the Safety Pro...

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1st International Workshop on database Technology and Applications

作者： Han Xiao-bing Tian Feng Wu Feng-bo Xian Univ Sci & Technol Coll Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9780769536040

In order to achieve the integration interactive of heterogeneous data in the safety production management used by coal-mining enterprises, effectively improve the enterprise's mining safety and management. Through analyzing the heterogeneous data in mining safety and management, on the basis of XML based business model of heterogeneous data integration, formed coal-mining safety management in heterogeneous data integration framework, succeed in making converse between relative data and XML. We used XML and Javabean, designed and achieved function in the application layer. By analyzing the query results of system running and return, the system solved the heterogeneous data integration in the safety production management of coal-mining.

关键词： safety production XML heterogeneous data heterogeneous data integration data integration

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heterogeneous data integration FRAMEWORK BASED ON GRID SERVICE

HETEROGENEOUS DATA INTEGRATION FRAMEWORK BASED ON GRID SERVI...

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Yanbing Liu Zhangxiong Liu Laiming Luo School of Computer Science Chongqing University of Posts and Telecommunications Chongqing ChongQing Jugou Technology Development Co. Ltd.Chongqing

ISBN: (纸本)9781424448999;9781424448982

关键词： heterogeneous data integration grid technique IMS grid service

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heterogeneous data integration FRAMEWORK BASED ON GRID SERVICE

HETEROGENEOUS DATA INTEGRATION FRAMEWORK BASED ON GRID SERVI...

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2009 IEEE International Conference on Network Infrastructure and Digital Content（IC-NIDC 2009）

作者： Yanbing Liu1,Zhangxiong Liu1,Laiming Luo2 1.School of Computer Science,Chongqing University of Posts and Telecommunications,Chongqing 2.ChongQing Jugou Technology Development Co.,Ltd.,Chongqing

heterogeneous data integration plays an important role in the information management system (IMS).Traditional IMS have low integration degree and are difficult to adapt to dynamic changes of databases *** this paper,we propose a grid service framework for heterogeneous data *** framework consists of three layers,including data service layer,data integration layer and data display *** data integration layer is the core of the whole *** IMS is implemented according to the proposed framework,and massive data are used for the system implementation to verify the correctness of results *** performance evaluations are reported to establish the superiority of the grid technique in terms of the round-trip time and the CPU occupancy.

关键词： heterogeneous data integration grid technique IMS grid service

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