Predicting Cancer Types From miRNA Stem-loops Using Deep Learning

Laplante, Jean-François; Akhloufi, Moulay A.

doi:10.1109/embc44109.2020.9176345

Cited by 12 publications

(14 citation statements)

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“…Applications of DL to assist physicians and scientists in clinical settings using epigenomic data have been relatively unexplored until very recently. Except for one paper published in 2016, 21 out of 22 papers reviewed were published in the last 5 years and a majority of the models were developed by USA and China research teams [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This suggests a novel field of study that gains an increasing interest.…”

Section: Resultsmentioning

confidence: 99%

“…Various network architectures were employed such as multi-layer perceptron, autoencoder and its variants, convolutional neural network, and deep belief network. Reviewed models yielded high accuracy ranged from 88.3% to 100.0% for disease detection tasks [ 23 , 24 , 28 , 29 , 30 , 31 ], from 69.5% to 97.8% for subtype classification tasks [ 32 , 33 , 35 , 36 , 37 , 38 , 40 ], and from 80.0% to 93.0% for treatment response prediction tasks [ 41 , 42 ].…”

Section: Resultsmentioning

confidence: 99%

“…Laplante et al [ 35 ] developed a DNN classifier with the first layer consisting of 1046 input neurons for each stem-loop miRNA count and the final layer consisting of 27 neurons for each different type of cancer. Tumors in 20 anatomical sites were classified with 96.88% of accuracy, demonstrating the potential of miRNA data for an accurate cancer localization.…”

Section: Resultsmentioning

confidence: 99%

“…For these reasons, miRNAs can be considered to be important players in the epigenetic control of gene expression [ 53 ]. Reviewed studies proved their potential efficacy as non-invasive, specific, and sensitive biomarkers for disease diagnosis [ 21 , 22 , 27 ], subtype classification [ 35 ], and treatment response prediction [ 42 ].…”

Section: Resultsmentioning

confidence: 99%

“…In relation with miRNA stem-loop counts obtained from public databases, high correlations could be produced among some of the datasets. In such cases, the highly correlated datasets were grouped together based on anatomical site [ 35 ]. Furthermore, for the miRNA expression level, Afshar et al [ 21 ] removed miRNAs with signal-to-noise ratio smaller than or equal to 2.5, whereas Elias et al [ 27 ] selected a detection threshold at 10 tags per million read.…”

Section: Resultsmentioning

confidence: 99%

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Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Nguyen

Kim

et al. 2021

Biomedicines

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Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Nguyen

Kim

et al. 2021

Biomedicines

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Popular deep learning algorithms for disease prediction: a review

Wang

Wan

et al. 2022

Cluster Comput

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Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.

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