ReviewFrontiers in oncology2020
Computational Oncology in the Multi-Omics Era: State of the Art.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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
46 citing papers in PubMed, 96 citations in OpenAlex.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Oncogenic cell fate decision in breast epithelial cells I: growth factors and mechanisms.Research square · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Unraveling the mechanisms of LncRNAs in cervical cancer: a comprehensive review.Cell death discovery · 2025Review
- Unvaporized e-liquid toxicity elevates CD44-dependent hyaluronan catabolic gene expression and triggers inflammation in human vocal fold fibroblasts.Toxicology reports · 2025Article
- DSCC: disease subtyping using spectral clustering and community detection from consensus networks.Briefings in bioinformatics · 2025Article
- Deciphering and targeting oncogenic pathways through integrated approaches and amino acid metabolism in hematologic malignancies.Discover oncology · 2025Review
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Integromics profiling of oral carcinoma: exploring the role of miRNAs and circRNAs.Frontiers in oral health · 2025Review
- What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence.Nucleic acids research · 2024Article
- Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease.Cell reports. Medicine · 2024Review
- Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.Toxicology and applied pharmacology · 2024Article
- Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.Journal of biological methods · 2024Review
- Current state and future prospects of spatial biology in colorectal cancer.Frontiers in oncology · 2024Review
- A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers.International journal of molecular sciences · 2023Article
- Omics-Based Investigations of Breast Cancer.Molecules (Basel, Switzerland) · 2023Review
- Molecular mechanisms of multi-omic regulation in breast cancer.Frontiers in oncology · 2023Review
- Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases.Fundamental research · 2022Review
- COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients.Scientific data · 2022Article
- Connecting omics signatures and revealing biological mechanisms with iLINCS.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.
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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.