Evidence map›Paper›PMID 25750696›Full record

ReviewComputational and structural biotechnology journal2015

Machine learning applications in cancer prognosis and prediction.

Konstantina Kourou, Themis P Exarchos, Konstantinos P Exarchos, Michalis V Karamouzis, Dimitrios I Fotiadis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computational and structural biotechnology journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,035 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,035citing papers in PubMed, 4 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

1,035 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Polygenic Risk Scores for Myopia: A Systematic Review of Predictive Performance and Clinical Potential.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review

975 more citing papers are in PubMed but not listed here.

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.

Konstantina KourouUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
Themis P ExarchosUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece ; IMBB - FORTH, Dept. of Biomedical Research, Ioannina, Greece.
Konstantinos P ExarchosUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
Michalis V KaramouzisMolecular Oncology Unit, Department of Biological Chemistry, Medical School, University of Athens, Athens, Greece.
Dimitrios I FotiadisUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece ; IMBB - FORTH, Dept. of Biomedical Research, Ioannina, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Indexed as

ANN, Artificial Neural NetworkAUC, Area Under CurveBCRSVM, Breast Cancer Support Vector MachineBN, Bayesian NetworkCancer recurrenceCancer survivalCancer susceptibilityCFS, Correlation based Feature SelectionDT, Decision TreeES, Early Stopping algorithmGEO, Gene Expression OmnibusHTT, High-throughput TechnologiesLCS, Learning Classifying SystemsMachine learningML, Machine LearningNCI caArray, National Cancer Institute Array Data Management SystemNSCLC, Non-small Cell Lung CancerOSCC, Oral Squamous Cell CarcinomaPPI, Protein–Protein InteractionPredictive modelsROC, Receiver Operating CharacteristicSEER, Surveillance, Epidemiology and End results DatabaseSSL, Semi-supervised LearningSVM, Support Vector MachineTCGA, The Cancer Genome Atlas Research Network

Identifiers

PMID25750696
PMCPMC4348437

What OpenQuestion 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 OpenQuestion graph.