ArticleDiscover oncology2026
Technological synergy and convergent innovation: evolutionary trajectories of AI-integrated multi-omics in cancer research and clinical translation (2015-2024).
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis bibliometric analysis systematically examined cancer research literature (2015-2024), focusing on advancements driven by machine learning, multi-omics, and artificial intelligence (AI).
methodsCiteSpace 6.3.R1 was used to construct co-citation, keyword co-occurrence, and collaboration networks, with knowledge graph optimization conducted via the g-index (k = 10) and LRF = 3.0 parameters.
resultsThe publication volume demonstrated a biphasic growth. From 2015 to 2019, the average annual growth rate was 45%, increasing to 65% post-2020. By 2024, publications reached 916, a 25.4-fold increase since 2015. This trend coincided with expanding policy support and technological advances, though direct causality remains to be established. Moreover, core journals (e.g., Nature, Cell, Cancer Research) sustained substantial influence via high centrality values (mean = 0.28), fostering cross-domain knowledge exchange. Meanwhile, emerging journals such as PLOS ONE (burst strength = 56.16) mirrored open science trends and advances in proteomic technologies. The technological landscape unfolded through three phases: metabolomics dominance (2015-2017, MS burst = 16.83), AI integration (2018-2021, ML centrality = 0.11), and precision medicine expansion (2022-2024, scRNA-seq growing 182% annually). In most of the clusterings, the emerging technology of artificial intelligence is involved, notably in studies predicting drug responses.
conclusionsOverall, these bibliometric findings indicate that AI-integrated multi-omics constitutes an emerging focus in studies modeling the tumor microenvironment. However, further empirical research is warranted to evaluate its actual clinical or biological relevance.
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Registered trials
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.