Evidence map›Paper›PMID 37258923›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development.

P Preeti, Swarsat Kaushik Nath, Nevidita Arambam, Trapti Sharma, Priyanka Ray Choudhury, Alakto Choudhury, Vrinda Khanna, Ulrich Strych, Peter J Hotez, Maria Elena Bottazzi and 1 more

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

P PreetiCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Swarsat Kaushik NathCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Nevidita ArambamCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Trapti SharmaCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Priyanka Ray ChoudhuryCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Alakto ChoudhuryCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Vrinda KhannaCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
Ulrich StrychDepartment of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA.
Peter J HotezDepartment of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA.
Maria Elena BottazziDepartment of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA.
Kamal RawalCentre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India. krawal@amity.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .

Indexed as

Artificial IntelligenceVaccinesAnimalsAntigensProteinsVaccine DevelopmentAntigensProteinsVaccinesAntigen predictionArtificial intelligenceCOVID-19Deep learningDeep learning pathogen modelsIn silico vaccine developmentMachine learningmRNA vaccinesVaccineVaccine designVaxi-DL

Identifiers

PMID37258923

What OpenQuestion holds

Textmetadata
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.