Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance

Macías-García, Laura; Martínez-Ballesteros, M.; Luna-Romera, José María; García-Heredia, José Manuel; García-Gutiérrez, Jorge; Santos, José Cristóbal Riquelme

doi:10.1016/j.artmed.2020.101976

Cited by 33 publications

(33 citation statements)

References 73 publications

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“…A few diagnostic measures utilizing cancer-specific DNA methylation patterns have already received FDA approval (78,79). Moreover, ML and DL analyses have been increasingly used to identify novel disease-specific DNA methylation patterns; they have also been used in research that aims to utilize the DNA methylation data from cancer patients for diagnosis, staging, and prognosis predictions (80)(81)(82)(83).…”

Section: Dna Methylationmentioning

confidence: 99%

“…Authors developed models that classified 96.4% of the cases by NN, 95.7% by SVM, and 87.8% by RF ( 82 ). The DL-based approach is also used to detect DNA methylation patterns related to breast cancer metastases and predict recurrence by conducting feature selection using an autoencoder with a single hidden layer followed by ML techniques for classification, or enrichment analysis for finding a biological relevance, genomic context, and functional annotation of best genes ( 83 ).…”

Section: Dna Methylationmentioning

confidence: 99%

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Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

et al. 2021

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With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, “precision medicine,” which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

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Section: Dna Methylationmentioning

confidence: 99%

Section: Dna Methylationmentioning

confidence: 99%

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

et al. 2021

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“…The method is based on AEs to preprocess DNA methylation and generate AE features to characterize breast cancer recurrence and demonstrated how it improved recurrence prediction. 51 AI in cervical cancer: Out of half million annual cervical cancer cases in the world about 80% occur in low and middle income nations. Hu et al followed over 9,000 women ages 18 to 94 from Costa Rica over period of seven years from 1993 to 2000 identifying cancers up to 18 years.…”

Section: E) Convolutional Neural Network (Cnn) In Pathologymentioning

confidence: 99%

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Buch¹,

Kulkarni²

2021

GJMR

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This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

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“…Papers by Macias-Garcia et al. [ 16 ], and Pozzoli et al. [ 17 ] offer significant contributions to the methodological side of the big data topic.…”

mentioning

confidence: 99%

“…Papers by Macias-Garcia et al [16], and Pozzoli et al [17] offer significant contributions to the methodological side of the big data topic. Macias-Garcia et al summarize DNA methylation data to generate new features from the values of CpG sites of patients, to predict breast cancer recurrence.…”

mentioning

confidence: 99%

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care

et al. 2021

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Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance

Cited by 33 publications

References 73 publications

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care

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