Computational prediction of gene function
基因功能的计算预测
基本信息
- 批准号:327585-2011
- 负责人:
- 金额:$ 4.08万
- 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:2015
- 资助国家:加拿大
- 起止时间:2015-01-01 至 2016-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Over the next five years, tens of thousands of new organisms will have their genomes sequenced. These new data present a substantial challenge and an unprecedented opportunity.
The new challenge is answering the question: How do we understand these genomes? Although we can identify the genes within them, how can we tell what the genes do? Much about the function of a gene is difficult to extract from genome sequence alone and requires further experimentation to determine. However, this type of experimentation is much more difficult and costly than genome sequencing. Traditionally, scientists have focused on a small number of representative "model" organisms to perform these more detailed experiments. However, there is not always a clear link between gene function in model organisms and that in other, closely related ones. One focus of this proposal is to attempt to define the conditions under which we can confidently predict that a gene's function is conserved between two organisms. We will accomplish this goal by training computational algorithms to predict when gene function is conserved between two model organisms. In this situation, we know the correct answer and can therefore evaluate how well our methods are doing before applying them to situations in which we do not know the correct answer.
The exciting part of this opportunity is that we can use these new data to help us to analyze biological samples in terms of what cells are contained within. Such a technique has a wide variety of uses, ranging from analyzing cancerous tumours to detecting infectious agents in blood. We will pursue this opportunity by first constructing a library of cell type specific signatures from different organisms and then using that library, along with a new computational technique (that we will develop), we will analyze new biological samples.
These two projects represent excellent thesis projects for two or more students; and we predict that if successful, they will have a substantial and lasting impact on the scientific community and, potentially, society in general.
在接下来的五年里,数以万计的新生物的基因组将被测序。这些新数据既带来了重大挑战,也带来了前所未有的机遇。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Morris, Quaid其他文献
LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance.
- DOI:
10.1016/j.molcel.2012.08.004 - 发表时间:
2012-10-26 - 期刊:
- 影响因子:16
- 作者:
Wilbert, Melissa L.;Huelga, Stephanie C.;Kapeli, Katannya;Stark, Thomas J.;Liang, Tiffany Y.;Chen, Stella X.;Yan, Bernice Y.;Nathanson, Jason L.;Hutt, Kasey R.;Lovci, Michael T.;Kazan, Hilal;Vu, Anthony Q.;Massirer, Katlin B.;Morris, Quaid;Hoon, Shawn;Yeo, Gene W. - 通讯作者:
Yeo, Gene W.
The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis.
- DOI:
10.1002/art.38875 - 发表时间:
2014-12 - 期刊:
- 影响因子:13.3
- 作者:
Eng, Simon W. M.;Duong, Trang T.;Rosenberg, Alan M.;Morris, Quaid;Yeung, Rae S. M. - 通讯作者:
Yeung, Rae S. M.
PLIDA: cross-platform gene expression normalization using perturbed topic models
- DOI:
10.1093/bioinformatics/btt574 - 发表时间:
2014-04-01 - 期刊:
- 影响因子:5.8
- 作者:
Deshwar, Amit G.;Morris, Quaid - 通讯作者:
Morris, Quaid
RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors
- DOI:
10.1093/bioinformatics/btm224 - 发表时间:
2007-07-01 - 期刊:
- 影响因子:5.8
- 作者:
Chen, Xiaoyu;Hughes, Timothy R.;Morris, Quaid - 通讯作者:
Morris, Quaid
A deep learning approach reveals unexplored landscape of viral expression in cancer.
- DOI:
10.1038/s41467-023-36336-z - 发表时间:
2023-02-11 - 期刊:
- 影响因子:16.6
- 作者:
Elbasir, Abdurrahman;Ye, Ying;Schaffer, Daniel E.;Hao, Xue;Wickramasinghe, Jayamanna;Tsingas, Konstantinos;Lieberman, Paul M.;Long, Qi;Morris, Quaid;Zhang, Rugang;Schaffer, Alejandro A.;Auslander, Noam - 通讯作者:
Auslander, Noam
Morris, Quaid的其他文献
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{{ truncateString('Morris, Quaid', 18)}}的其他基金
Machine learning and biology
机器学习和生物学
- 批准号:
RGPIN-2019-07308 - 财政年份:2021
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Machine learning and biology
机器学习和生物学
- 批准号:
RGPIN-2019-07308 - 财政年份:2020
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Machine learning and biology
机器学习和生物学
- 批准号:
RGPIN-2019-07308 - 财政年份:2019
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Computational prediction of gene function
基因功能的计算预测
- 批准号:
327585-2011 - 财政年份:2014
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Computational prediction of gene function
基因功能的计算预测
- 批准号:
327585-2011 - 财政年份:2013
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Computational prediction of gene function
基因功能的计算预测
- 批准号:
327585-2011 - 财政年份:2012
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Computational prediction of gene function
基因功能的计算预测
- 批准号:
327585-2011 - 财政年份:2011
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Machine learning to support biological discovery
机器学习支持生物发现
- 批准号:
327585-2010 - 财政年份:2010
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Enabling systesm biology through machine learning: intelligent software to automatically summarize and combine large-scale biological databases
通过机器学习赋能系统生物学:智能软件自动汇总并组合大规模生物数据库
- 批准号:
327585-2006 - 财政年份:2008
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
Enabling systesm biology through machine learning: intelligent software to automatically summarize and combine large-scale biological databases
通过机器学习赋能系统生物学:智能软件自动汇总并组合大规模生物数据库
- 批准号:
327585-2006 - 财政年份:2007
- 资助金额:
$ 4.08万 - 项目类别:
Discovery Grants Program - Individual
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