Evidence map›Paper›PMID 34581805›Full record

ArticleNucleic acids research2021

BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models.

Hong-Liang Li, Yi-He Pang, Bin Liu

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
126citing papers in PubMed, 2 pooled it
13.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

126 citing papers in PubMed, 2 syntheses or guidelines pooled it, 244 citations in OpenAlex.

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66 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Hong-Liang LiSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Yi-He PangSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Bin LiuSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.ORCID 0000-0003-3685-9469
Beijing Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to uncover the meanings of 'book of life', 155 different biological language models (BLMs) for DNA, RNA and protein sequence analysis are discussed in this study, which are able to extract the linguistic properties of 'book of life'. We also extend the BLMs into a system called BioSeq-BLM for automatically representing and analyzing the sequence data. Experimental results show that the predictors generated by BioSeq-BLM achieve comparable or even obviously better performance than the exiting state-of-the-art predictors published in literatures, indicating that BioSeq-BLM will provide new approaches for biological sequence analysis based on natural language processing technologies, and contribute to the development of this very important field. In order to help the readers to use BioSeq-BLM for their own experiments, the corresponding web server and stand-alone package are established and released, which can be freely accessed at http://bliulab.net/BioSeq-BLM/.

Indexed as

SoftwareDeoxyribonuclease IDNA-Binding ProteinsIntrinsically Disordered ProteinsMicroRNAsModels, StatisticalNatural Language ProcessingNucleic Acid ConformationRNA-Binding ProteinsRNA PrecursorsSequence Analysis, DNASequence Analysis, ProteinSequence Analysis, RNADeoxyribonuclease IDNA-Binding ProteinsIntrinsically Disordered ProteinsMicroRNAsRNA-Binding ProteinsRNA Precursors

Identifiers

PMID34581805
PMCPMC8682797
OpenAlexW3203741962

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.