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Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature

Koksal, A.; Donmez, H.; Ozcelik, R.; Ozkirimli, E.; Ozgur, A.

2020-10-13 bioinformatics
10.1101/2020.09.05.284224 bioRxiv
Show abstract

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.5%
31.4%
Similar papers in this journal
2
Bioinformatics Advances
◐ 203 papers in training set
Top 0.4%
8.0%
Similar papers in this journal
3
Database
◐ 61 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
4
BioData Mining
○ 22 papers in training set
Top 0.1%
5.2%
Similar papers in this journal
50% of probability mass above
5
Journal of Cheminformatics
○ 29 papers in training set
Top 0.2%
4.4%
Similar papers in this journal
6
BMC Bioinformatics
○ 457 papers in training set
Top 2%
4.1%
7
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 1%
3.6%
8
Scientific Reports
○ 3612 papers in training set
Top 41%
2.4%
9
Patterns
○ 78 papers in training set
Top 0.8%
2.4%
10
GigaScience
◐ 212 papers in training set
Top 2%
2.1%
11
PLOS ONE
● 5266 papers in training set
Top 44%
1.9%
12
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.4%
1.9%
13
Computers in Biology and Medicine
○ 128 papers in training set
Top 2%
1.7%
14
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.6%
1.5%
15
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.4%
16
Nucleic Acids Research
◐ 1281 papers in training set
Top 11%
1.1%
17
Scientific Data
○ 209 papers in training set
Top 2%
1.1%
18
iScience
○ 1154 papers in training set
Top 32%
1.0%
19
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 2%
0.9%
20
Artificial Intelligence in the Life Sciences
○ 13 papers in training set
Top 0.3%
0.9%
21
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 5%
0.6%
22
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 2%
0.6%
23
Artificial Intelligence in Medicine
○ 17 papers in training set
Top 0.8%
0.6%
24
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 2%
0.6%
25
Frontiers in Genetics
○ 230 papers in training set
Top 6%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.