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LitRev: A data driven method for quick literature review from PubMed
Das, G.
2021-12-10
bioinformatics
10.1101/2021.12.07.471694
bioRxiv
Show abstract
SummaryLitRev is a novel robust data driven approach, developed for quick literature review on a particular topic of interest. This method identifies common biological phrases that follow a power law distribution and important phrases which have the normalized point wise mutual information score greater than zero. Contactgourabdas0727@gmail.com
Matching journals
●Non-profit
◐University press
○Commercial
The top 9 journals account for 50% of the predicted probability mass.
1
BMC Bioinformatics
○
457 papers in training set
Top 0.6%
10.9%
Similar papers in this journal
2
PLOS ONE
●
5266 papers in training set
Top 21%
7.8%
Similar papers in this journal
- Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques 94%
- Antivirals for Monkeypox Virus: Proposing an Effective Machine/Deep Learning Framework 94%
- GenomeBits insight into omicron and delta variants of coronavirus pathogen 93%
3
Briefings in Bioinformatics
◐
354 papers in training set
Top 2%
5.4%
Similar papers in this journal
- DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences 95%
- An in silico approach to identification, categorization and prediction of nucleic acid binding proteins 94%
- Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models 94%
4
Scientific Reports
○
3612 papers in training set
Top 16%
5.4%
Similar papers in this journal
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 95%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 95%
- Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy 95%
5
Computational and Structural Biotechnology Journal
●
242 papers in training set
Top 0.7%
5.1%
Similar papers in this journal
- iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm 94%
- AutoVEM2: a flexible automated tool to analyze candidate key mutations and epidemic trends for virus 93%
- SpatialPPI: three-dimensional space protein-protein interaction prediction with AlphaFold Multimer 93%
50% of probability mass above
"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.