Evidence map›Paper›PMID 39436606›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2025

Proteogenomic Approaches for Diseasome Studies.

Medhavi Pokhriyall, Nidhi Shukla, Tiratha Raj Singh, Prashanth Suravajhala

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2025. 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

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

4 authors.

Medhavi Pokhriyall *Centre of Excellence in Healthcare Technologies and Informatics (CEHTI), Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan, India.
Nidhi Shukla *Dr. B. Lal Institute of Biotechnology, Jaipur, India.
Tiratha Raj SinghCentre of Excellence in Healthcare Technologies and Informatics (CEHTI), Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan, India.
Prashanth SuravajhalaAmrita School of Biotechnology, Amrita Vishwa Vidyapeethaam, Amritapuri, Clappana, Kerala, India. prash@am.amrita.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Indexed as

High-Throughput Nucleotide SequencingProteogenomicsAnimalsComputational BiologyGenomicsHumansProteomeProteomicsSoftwareProteomeDiseasomeMachine learningNGSProteogenomicProteomics

Identifiers

PMID39436606

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.