A content-based dataset recommendation system for researchers—a case study on Gene Expression Omnibus (GEO) repository

Abstract

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/#/

Taxonomic Description and Genome Sequence of Christensenella intestinihominis sp. nov., a Novel Cholesterol-Lowering Bacterium Isolated From Human Gut

“…The draft genome of C. intestinihominis AF73-05CM02 T has been deposited at DDBJ/EMBL/GenBank under the accession number MAIQ00000000 . The data that support the findings of this study have also been deposited into CNGB Sequence Archive (CNSA) ( Guo et al, 2020 ) of China National GeneBank DataBase (CNGBdb) ( Chen et al, 2020 ) with accession number CNPhis0003415 .…”

The Dfam community resource of transposable element families, sequence models, and genome annotations

“…FamDB files contain family consensi/HMMs and the NCBI Taxonomy data related to these families in a format that allows for fast offline access from the command line. The current release of FamDB includes all Dfam consensus sequences, HMMs, metadata, and 61,003 taxa from NCBI’s taxonomy database [ 46 ] related to these families. Lookups for information on a single taxon or family complete in about a second; extraction of consensus sequences (FASTA, EMBL) or HMMs for all TE families found in Human (including ancestral repeats) complete in about 3 to 4 s. Due to indexing, the run time for data queries is largely independent of the total number of TEs in the database: it takes about the same amount of time to extract the human library from a FamDB file including only the curated subset of Dfam (6915 entries) as for the full database (273,655).…”

Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

“…In the RA field, a fully annotated, expert validated, state-of-the-art knowledge base in the form of a molecular map has been published recently, illustrating the molecular and signaling pathways involved in disease pathogenesis [ 183 , 184 ]. However, this map is not cell-specific as it includes experiments in different cell types such as mononuclear cells, synovial fibroblasts, macrophages and chondrocytes.…”

“…In RA, the recently published RA map [ 184 ] can serve as a basis for the building of a regulatory graph and the associated logical model. Initially, researchers were set to build a large-scale boolean dynamical model for the study of RA fibroblasts’ activation based on the RA map and a previously published, more generic model on fibroblasts [ 213 ].…”