Novel methods for integrative computational biology

综合计算生物学的新方法

基本信息

  • 批准号:
    RGPIN-2018-05757
  • 负责人:
  • 金额:
    $ 2.99万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Current challenges in biomedical research include information overload: the need to combine vast amounts of structured and unstructured information, and the need to identify, characterize, optimize, link, validate and interpret patterns in complex data. The majority of biological data and relationships between biological entities is conveniently modeled as a graph. Graph databases provide a high-performance solution to integrating diverse entities and providing a scalable platform for machine learning and inference. Data mining, graph theory and ontologies are essential components of successful learning systems. Quality, coverage and annotation of these typed graphs provide the necessary platform for model and hypotheses generation.We propose to develop a scalable, distributed architecture to store, analyze integrated graph data to support advance machine learning and data modeling. This resource will provide comprehensive coverage of available proteome, federate and map entities to a database of experimentally detected, orthologous, and predicted tissue- and condition-specific protein interactions. We will improve and extended our association mining algorithm, FpClass, a machine learning system for weighted link prediction between interacting proteins. We will extend the method to predict links of various types between genes and proteins under the integrated graphical model in sequence, tissue, and condition-specific contexts using the Turing Complete and performance-optimized graph query language Gremlin.Ontologies will be used to support multiple viewpoints and contexts. Graph theory and databases will provide necessary infrastructure and complex pattern inference platform. Machine learning and data mining will contribute new models, while probabilistic modeling will support handling incomplete, contradictory and ambiguous information. We will use NAViGaTOR to support visual data mining and interactive exploration of the typed graphs.We will test and validate developed platforms using multiple, publicly available datasets. This research will generate novel algorithms, and after validation, their application will lead to improved understanding of complex diseases on a molecular level. Algorithms, results and relevant data will be made publicly available to encourage further computational research in this increasingly important area. Together with deep learning, these approaches are revolutionizing application areas since the performance of these systems frequently exceed domain experts' abilities and biological assay sensitivity and false discovery rates.The proposed research will advance computational approaches and their applicability to high-throughput systems biology applications. An important function of this proposal is the training of bioinformatics professionals for which there is still great unfulfilled demand.
当前生物医学研究面临的挑战包括信息过载:需要将大量的结构化和非结构化信息联合收割机结合起来,需要识别、表征、优化、链接、验证和解释复杂数据中的模式。大多数生物数据和生物实体之间的关系被方便地建模为图。图形数据库提供了一种高性能的解决方案,可以集成不同的实体,并为机器学习和推理提供可扩展的平台。数据挖掘、图论和本体论是成功的学习系统的重要组成部分。这些类型化图的质量、覆盖率和注释为模型和假设生成提供了必要的平台,我们建议开发一个可扩展的分布式架构来存储、分析集成图数据,以支持先进的机器学习和数据建模。该资源将提供全面覆盖可用的蛋白质组,联邦和地图实体的实验检测,正交和预测的组织和条件特异性蛋白质相互作用的数据库。我们将改进和扩展我们的关联挖掘算法FpClass,这是一个用于相互作用蛋白质之间加权链接预测的机器学习系统。我们将使用图灵完备和性能优化的图查询语言Gremlin,在序列、组织和条件特定的背景下,在集成图形模型下扩展该方法来预测基因和蛋白质之间的各种类型的链接。本体将用于支持多个观点和背景。图论和数据库将提供必要的基础设施和复杂模式推理平台。机器学习和数据挖掘将提供新的模型,而概率建模将支持处理不完整,矛盾和模糊的信息。我们将使用NAViGaTOR来支持可视化数据挖掘和交互式探索的类型图。我们将测试和验证开发平台使用多个,公开可用的数据集。这项研究将产生新的算法,经过验证,它们的应用将导致在分子水平上更好地理解复杂疾病。算法,结果和相关数据将公开提供,以鼓励在这一日益重要的领域进行进一步的计算研究。与深度学习一起,这些方法正在彻底改变应用领域,因为这些系统的性能经常超过领域专家的能力和生物测定灵敏度和错误发现率。拟议的研究将推进计算方法及其在高通量系统生物学应用中的适用性。该提案的一个重要功能是培训生物信息学专业人员,目前仍有很大的需求未得到满足。

项目成果

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Jurisica, Igor其他文献

Effect of autotaxin inhibition in a surgically-induced mouse model of osteoarthritis.
  • DOI:
    10.1016/j.ocarto.2020.100080
  • 发表时间:
    2020-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Datta, Poulami;Gandhi, Rajiv;Nakamura, Sayaka;Lively, Starlee;Rossomacha, Evgeny;Potla, Pratibha;Shestopaloff, Konstantin;Endisha, Helal;Pastrello, Chiara;Jurisica, Igor;Rockel, Jason S;Kapoor, Mohit
  • 通讯作者:
    Kapoor, Mohit
MirDIP 5.2: tissue context annotation and novel microRNA curation.
  • DOI:
    10.1093/nar/gkac1070
  • 发表时间:
    2023-01-06
  • 期刊:
  • 影响因子:
    14.9
  • 作者:
    Hauschild, Anne-Christin;Pastrello, Chiara;Ekaputeri, Gitta Kirana Anindya;Bethune-Waddell, Dylan;Abovsky, Mark;Ahmed, Zuhaib;Kotlyar, Max;Lu, Richard;Jurisica, Igor
  • 通讯作者:
    Jurisica, Igor
Protein interaction data curation: the International Molecular Exchange (IMEx) consortium.
  • DOI:
    10.1038/nmeth.1931
  • 发表时间:
    2012-04
  • 期刊:
  • 影响因子:
    48
  • 作者:
    Orchard, Sandra;Kerrien, Samuel;Abbani, Sara;Aranda, Bruno;Bhate, Jignesh;Bidwell, Shelby;Bridge, Alan;Briganti, Leonardo;Brinkman, Fiona;Cesareni, Gianni;Chatr-aryamontri, Andrew;Chautard, Emilie;Chen, Carol;Dumousseau, Marine;Goll, Johannes;Hancock, Robert;Hannick, Linda I.;Jurisica, Igor;Khadake, Jyoti;Lynn, David J.;Mahadevan, Usha;Perfetto, Livia;Raghunath, Arathi;Ricard-Blum, Sylvie;Roechert, Bernd;Salwinski, Lukasz;Stuempflen, Volker;Tyers, Mike;Uetz, Peter;Xenarios, Ioannis;Hermjakob, Henning
  • 通讯作者:
    Hermjakob, Henning
The let-7b-5p, miR-326, and miR-125a-3p are associated with left ventricular systolic dysfunction in post-myocardial infarction.
LET-7B-5P,miR-326和miR-125a-3p与腰椎后梗死中的左心室收缩功能障碍有关。
  • DOI:
    10.3389/fcvm.2023.1151855
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Dantas-Komatsu, Raquel Costa Silva;Cruz, Marina Sampaio;Freire, Paula Paccielli;Diniz, Rosiane Viana Zuza;Bortolin, Raul Hernandes;Cabral-Marques, Otavio;Souza, Kamilla Batista da Silva;Hirata, Mario Hiroyuki;Hirata, Rosario Dominguez Crespo;Reis, Bruna Zavarize;Jurisica, Igor;Silbiger, Vivian Nogueira;Luchessi, Andre Ducati
  • 通讯作者:
    Luchessi, Andre Ducati
Prediction of Protein-Protein Interactions.
  • DOI:
    10.1002/cpbi.38
  • 发表时间:
    2017-12-08
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kotlyar, Max;Rossos, Andrea E M;Jurisica, Igor
  • 通讯作者:
    Jurisica, Igor

Jurisica, Igor的其他文献

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{{ truncateString('Jurisica, Igor', 18)}}的其他基金

Novel methods for integrative computational biology
综合计算生物学的新方法
  • 批准号:
    RGPIN-2018-05757
  • 财政年份:
    2021
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Novel methods for integrative computational biology
综合计算生物学的新方法
  • 批准号:
    RGPIN-2018-05757
  • 财政年份:
    2020
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Novel methods for integrative computational biology
综合计算生物学的新方法
  • 批准号:
    RGPIN-2018-05757
  • 财政年份:
    2019
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Novel methods for integrative computational biology
综合计算生物学的新方法
  • 批准号:
    RGPIN-2018-05757
  • 财政年份:
    2018
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Developing novel heuristic methods for integrative computational biology
开发综合计算生物学的新颖启发式方法
  • 批准号:
    203833-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Developing novel heuristic methods for integrative computational biology
开发综合计算生物学的新颖启发式方法
  • 批准号:
    203833-2013
  • 财政年份:
    2016
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Developing novel heuristic methods for integrative computational biology
开发综合计算生物学的新颖启发式方法
  • 批准号:
    203833-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Developing novel heuristic methods for integrative computational biology
开发综合计算生物学的新颖启发式方法
  • 批准号:
    203833-2013
  • 财政年份:
    2014
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Developing novel heuristic methods for integrative computational biology
开发综合计算生物学的新颖启发式方法
  • 批准号:
    203833-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Techna 2012: Information and Communication Technologies for Health
Techna 2012:健康信息和通信技术
  • 批准号:
    436800-2012
  • 财政年份:
    2012
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Strategic Workshops Program

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复杂图像处理中的自由非连续问题及其水平集方法研究
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旨在改善脊柱手术后效果的新型综合正念和针灸计划的可行性试验(I-MASS)
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  • 财政年份:
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Novel methods for integrative computational biology
综合计算生物学的新方法
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    RGPIN-2018-05757
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综合计算生物学的新方法
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Novel methods for integrative computational biology
综合计算生物学的新方法
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    RGPIN-2018-05757
  • 财政年份:
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  • 资助金额:
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