Evidence map›Paper›PMID 37961379›Full record

ArticlebioRxiv : the preprint server for biology2023

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics.

Kumar Thurimella, Ahmed M T Mohamed, Daniel B Graham, Róisín M Owens, Sabina Leanti La Rosa, Damian R Plichta, Sergio Bacallado, Ramnik J Xavier

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 6 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 3 countries.

Kumar ThurimellaBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Ahmed M T MohamedBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Daniel B GrahamBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Róisín M OwensDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Sabina Leanti La RosaFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
Damian R PlichtaBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Sergio BacalladoDepartment of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK.
Ramnik J XavierBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Broad Institute · USUniversity of Cambridge · GBNorwegian University of Life Sciences · NO

Funding

Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI RAYMOND T CHUNG · 1991 to 2026
$35.3M
Determinants of inception of inflammation in inflammatory bowel diseasesR01DK127171 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI ANANTHAKRISHNAN, ASHWIN N, XAVIER, RAMNIK J · 2021 to 2025
$5.4M
Cardiovascular disease, metabolic syndrome, microbes and metabolites in FHSR01HL157717 · NHLBI · BROAD INSTITUTE, INC. · PI XAVIER, RAMNIK J · 2022 to 2025
$3.9M
NHLBI NIH HHS R01 HL157717NIDDK NIH HHS P30 DK043351NIDDK NIH HHS R01 DK127171
6 · The paper itself

Abstract

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of

Identifiers

PMID37961379
PMCPMC10634757
OpenAlexW4388263226

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.