Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Bosquet, Jesús González; Gabrilovich, Sofia; McDonald, Megan; Smith, Brian J.; Leslie, Kimberly K.; Bender, David; Goodheart, Michael J.; Devor, Eric J.

doi:10.3390/ijms232416014

Cited by 5 publications

(2 citation statements)

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“…On the other hand, models focused on predicting patient prognosis integrate clinical and genomic data to obtain a more complete picture of the patient and assess the risk of disease progression. Previous studies, particularly in cancer, have shown how this integration of data provides a more comprehensive and accurate assessment of patient outcome 38,39 . Alternatively, models aimed at predicting possible pathogenic combinations of genes use features that summarize the association of these genes with the biological pathways in which they participate.…”

Section: Discussionmentioning

confidence: 99%

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Roman‐Naranjo

Parra-Perez

López-Escámez

2023

Preprint

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Background: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases. Methods: We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis. Findings: Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations. Conclusions: ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases. Keywords: artificial intelligence, rare diseases, precision medicine, rare variants, DNA-sequencing, genomics

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Section: Discussionmentioning

confidence: 99%

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Roman‐Naranjo

Parra-Perez

López-Escámez

2023

Preprint

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“…Data are shown as mean ± SD from three independent experiments. A two-tailed Student's t-test and ANOVA were used in the current study [ [34] , [35] , [36] ]. Statistical significance is indicated as *p < 0.05, **p < 0.01, and ***p < 0.001.…”

Section: Statisticsmentioning

confidence: 99%