Evidence map›Paper›PMID 36140175›Full record

ReviewBiomedicines2022

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice.

Serena Dotolo, Riziero Esposito Abate, Cristin Roma, Davide Guido, Alessia Preziosi, Beatrice Tropea, Fernando Palluzzi, Luciano Giacò, Nicola Normanno

Abstract readReview
In one paragraph

Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Leveraging artificial intelligence in next generation sequencing for head & neck cancer: opportunities and challenges.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
    Article
  8. Review
  9. Review
  10. Review
  11. Article
  12. Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
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

9 authors.

Serena DotoloCell Biology and Biotherapy Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
Riziero Esposito AbateCell Biology and Biotherapy Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
Cristin RomaCell Biology and Biotherapy Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
Davide GuidoBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy.
Alessia PreziosiBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy.ORCID 0000-0002-9164-3778
Beatrice TropeaBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy.
Fernando PalluzziBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy.
Luciano GiacòBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy.ORCID 0000-0003-0657-4323
Nicola NormannoCell Biology and Biotherapy Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.ORCID 0000-0002-7158-2605

Funding

5x1000 project Implementazione di un percorso di oncologia di precisione per pazienti con neoplasie avanzate"Alleanza Contro il Cancro GerSom projectMinistero della Salute Ricerca Corrente "Analisi mediante pannello multigenico per la predisposizione a sin-dromi ereditarie di pazienti con tumore gastrico".
6 · The paper itself

Abstract

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient's tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Indexed as

big databiological complexitycancerhomologous recombination deficiencyML/AI algorithmsnetwork analysisNGStargeted therapyvariant calling

Identifiers

PMID36140175
PMCPMC9495893

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.