Evidence map›Paper›PMID 38565775›Full record

ReviewMolecular biotechnology2025

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.

Arnab Mukherjee, Suzanna Abraham, Akshita Singh, S Balaji, K S Mukunthan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Molecular biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
–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

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Pharmaceutical biology · 2026
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  13. Exploring metabolic signatures in urine using NMR for improved prognosis of gliomas.Metabolomics : Official journal of the Metabolomic Society · 2026
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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

5 authors.

Arnab MukherjeeDepartment of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Suzanna AbrahamDepartment of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Akshita SinghDepartment of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
S BalajiDepartment of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
K S MukunthanDepartment of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. mukunthan.ks@manipal.edu.ORCID http://orcid.org/0000-0002-2147-5182

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Indexed as

Computational BiologyDrug DiscoveryGenomicsMolecular Targeted TherapyBig DataHumansMachine LearningMetabolomicsMultiomicsBig dataMachine learningMulti-omicsNetwork pharmacologyTargeted therapeutics

Identifiers

PMID38565775
PMCPMC11928429

What Socratic 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 Socratic graph.