Evidence map›Paper›PMID 39314346›Full record

ArticlebioRxiv : the preprint server for biology2024

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data.

Shuai Zeng, Trinath Adusumilli, Sania Zafar Awan, Manish Sridhar Immadi, Dong Xu, Trupti Joshi

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Shuai ZengDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
Trinath AdusumilliDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
Sania Zafar AwanMU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, 65211, USA.
Manish Sridhar ImmadiDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
Dong XuDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.ORCID 0000-0002-4809-0514
Trupti JoshiDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.

Funding

Multi-view self-supervised deep learning for biological sequences and beyondR35GM126985 · NIGMS · UNIVERSITY OF SOUTH FLORIDA · PI DONG XU · 2018 to 2026
$3.8M
NIGMS NIH HHS R35 GM126985
6 · The paper itself

Abstract

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

Indexed as

Automated hyperparameters tunningBiomarkerDeep learningMulti-omicsPhenotype predictionReproducibilityWeb-platform

Identifiers

PMID39314346
PMCPMC11418982

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.