A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository.
A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository.
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DOI：
10.1093/database/baaa064
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发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Wu H
中科院分区：
文献类型：
--
作者：
Patra BG;Roberts K;Wu H
为了实验的再现性和数据的可重用性，研究人员将他们的数据公开是一个日益增长的趋势。与其他研究人员共享数据有助于提高这项工作的可见度。另一方面，也有研究人员受到数据资源匮乏的制约。为了克服这一挑战，到目前为止已经建立了许多存储库和知识库，以方便数据共享。此外，在过去二十年中，添加到这些数据集库的数据集的数量呈指数级增长。然而，这些存储库大多是特定于领域的，没有一个可以向研究人员/用户推荐数据集。当然，对于研究人员来说，跟踪所有相关的存储库以供潜在使用是一件具有挑战性的事情。因此，根据以前的出版物向研究人员推荐数据集的数据集推荐系统可以提高他们的生产力，并加快进一步的研究。这项工作采用了一种信息检索(IR)范式来推荐数据集。我们假设，在语料库之外的数据集推荐和PubMed风格的生物医学IR之间存在两个根本的不同。首先，查询不是关键词，而是研究人员，体现在他或她的出版物中。其次，为了从无关的数据集中筛选出相关的数据集，研究人员更好地代表了一组兴趣，而不是他们的整个研究。第二种方法是使用非参数聚类技术实现的。这些聚类被用来使用出版物聚类的向量表示与数据集之间的余弦相似度来为每个研究人员推荐数据集。经过5位研究人员的人工评估，使用该方法得到的最大归一化折扣累积收益为10(NDCG@10)，精度为10(p@10)偏项，p@10严格为0.89，0.78和0.61。据我们所知，这是第一次对基于内容的数据集推荐进行此类研究。我们希望该系统将进一步促进数据共享，减少研究人员识别正确数据集的工作量，增加生物医学数据集的可重用性。数据库地址：http://genestudy.org/recommends/#/
It is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. Sharing data with fellow researchers helps in increasing the visibility of the work. On the other hand, there are researchers who are inhibited by the lack of data resources. To overcome this challenge, many repositories and knowledge bases have been established to date to ease data sharing. Further, in the past two decades, there has been an exponential increase in the number of datasets added to these dataset repositories. However, most of these repositories are domain-specific, and none of them can recommend datasets to researchers/users. Naturally, it is challenging for a researcher to keep track of all the relevant repositories for potential use. Thus, a dataset recommender system that recommends datasets to a researcher based on previous publications can enhance their productivity and expedite further research. This work adopts an information retrieval (IR) paradigm for dataset recommendation. We hypothesize that two fundamental differences exist between dataset recommendation and PubMed-style biomedical IR beyond the corpus. First, instead of keywords, the query is the researcher, embodied by his or her publications. Second, to filter the relevant datasets from non-relevant ones, researchers are better represented by a set of interests, as opposed to the entire body of their research. This second approach is implemented using a non-parametric clustering technique. These clusters are used to recommend datasets for each researcher using the cosine similarity between the vector representations of publication clusters and datasets. The maximum normalized discounted cumulative gain at 10 (NDCG@10), precision at 10 (p@10) partial and p@10 strict of 0.89, 0.78 and 0.61, respectively, were obtained using the proposed method after manual evaluation by five researchers. As per the best of our knowledge, this is the first study of its kind on content-based dataset recommendation. We hope that this system will further promote data sharing, offset the researchers’ workload in identifying the right dataset and increase the reusability of biomedical datasets. Database URL: http://genestudy.org/recommends/#/
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DOI：
10.1186/1747-5333-1-11
发表时间：
2006-08-22
期刊：
Journal of biomedical discovery and collaboration
影响因子：
--
作者：
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通讯作者：
Giannella E
DOI：
10.1093/jamia/ocx121
发表时间：
2018-03-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
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Chen X;Gururaj AE;Ozyurt B;Liu R;Soysal E;Cohen T;Tiryaki F;Li Y;Zong N;Jiang M;Rogith D;Salimi M;Kim HE;Rocca-Serra P;Gonzalez-Beltran A;Farcas C;Johnson T;Margolis R;Alter G;Sansone SA;Fore IM;Ohno-Machado L;Grethe JS;Xu H
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Xu H
DOI：
10.1093/database/bay019
发表时间：
2018-01-01
期刊：
Database : the journal of biological databases and curation
影响因子：
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作者：
Li Z;Li J;Yu P
通讯作者：
Yu P
影响因子：
3.7
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Achakulvisut T;Acuna DE;Ruangrong T;Kording K
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Kording K
影响因子：
4.5
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Patra, Braja Gopal;Maroufy, Vahed;Wu, Hulin
通讯作者：
Wu, Hulin