Evidence map›Paper›PMID 38002424›Full record

ArticleBioengineering (Basel, Switzerland)2023

Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline.

Nivedha Balakrishnan, Rahul Katkar, Peter V Pham, Taylor Downey, Prarthna Kashyap, David C Anastasiu, Anand K Ramasubramanian

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 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
0.2field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 2 institutions in 1 country.

Nivedha BalakrishnanDepartment of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.
Rahul KatkarDepartment of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.
Peter V PhamDepartment of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.
Taylor DowneyDepartment of Computer Science and Engineering, Santa Clara University, Santa Clara, CA 95053, USA.
Prarthna KashyapDepartment of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.
David C AnastasiuDepartment of Computer Science and Engineering, Santa Clara University, Santa Clara, CA 95053, USA.
Anand K RamasubramanianDepartment of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.ORCID 0000-0002-7057-8663
San Jose State University · USSanta Clara University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thrombin is a key enzyme involved in the development and progression of many cardiovascular diseases. Direct thrombin inhibitors (DTIs), with their minimum off-target effects and immediacy of action, have greatly improved the treatment of these diseases. However, the risk of bleeding, pharmacokinetic issues, and thrombotic complications remain major concerns. In an effort to increase the effectiveness of the DTI discovery pipeline, we developed a two-stage machine learning pipeline to identify and rank peptide sequences based on their effective thrombin inhibitory potential. The positive dataset for our model consisted of thrombin inhibitor peptides and their binding affinities (

Indexed as

anticoagulantantithromboticclassificationpeptide designregression

Identifiers

PMID38002424
PMCPMC10669389
OpenAlexW4388525309

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.