Evidence map›Paper›PMID 39453213›Full record

ArticleToxins2024

Optimizing Scorpion Toxin Processing through Artificial Intelligence.

Adam Psenicnik, Andres A Ojanguren-Affilastro, Matthew R Graham, Mohamed K Hassan, Mohamed A Abdel-Rahman, Prashant P Sharma, Carlos E Santibáñez-López

Abstract read
In one paragraph

Article in Toxins, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Adam PsenicnikDepartment of Biology, Western Connecticut State University, Danbury, CT 06810, USA.
Andres A Ojanguren-AffilastroDivisión Aracnología, Museo Argentino de Ciencias Naturales, Buenos Aires C1405, Argentina.
Matthew R GrahamDepartment of Biology, Eastern Connecticut State University, Willimantic, CT 06226, USA.ORCID 0000-0001-7192-1083
Mohamed K HassanZoology Department, Faculty of Science, Port Said University, Port Said 42521, Egypt.
Mohamed A Abdel-RahmanZoology Department, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt.ORCID 0000-0003-3862-5535
Prashant P SharmaDepartment of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.ORCID 0000-0002-2328-9084
Carlos E Santibáñez-LópezDepartment of Biology, Western Connecticut State University, Danbury, CT 06810, USA.ORCID 0000-0001-6062-282X

Funding

National Academy of Sciences 45890Science and Technology Development Fund 45890
6 · The paper itself

Abstract

Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt the opening/closing mechanisms in cell ion channels. These peptides are widely studied for several reasons including their use in drug discovery. Although improvements in RNAseq have greatly expedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due to their small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomes using a neural network approach. This pipeline implements basic neural networks to sort amino acid sequences to find those that are likely toxins and thereafter predict the type of toxin represented by the sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins in forthcoming scorpion genome sequencing projects and potentially serve a useful role in identifying targets for drug development.

Indexed as

Artificial IntelligenceScorpion VenomsAmino Acid SequenceAnimalsNeural Networks, ComputerScorpionsTranscriptomeScorpion Venomsneural networkpythonRNAseqsodium channel toxins

Identifiers

PMID39453213
PMCPMC11511117

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.