Deep learning based on multi-omics integration identifies potential therapeutic targets in breast cancer

Pan, Xingxin; Burgman, Brandon; Sahni, Nidhi; Yi, S. Stephen

doi:10.1101/2022.01.18.476842

Cited by 4 publications

(1 citation statement)

References 71 publications

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“…Another deep learning model along with multimodalities of data is used in [ 43 ] to predict Alzheimer's disease. The researchers in [ 44 ] have trained their deep learning model on multimodalities to predict therapeutic targets in breast cancer. A comprehensive comparison of multimodalities is presented in [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

Attique

Shah

Jabeen³

et al. 2022

Computational Intelligence and Neuroscience

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DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

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Section: Related Workmentioning

confidence: 99%

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

Attique

Shah

Jabeen³

et al. 2022

Computational Intelligence and Neuroscience

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Recent Advancements for Detection and Prediction of Breast Cancer Using Deep Learning: A Review?

Jyothi,

Koppu

2023

Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing

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AD-Syn-Net: systematic identification of Alzheimer’s disease-associated mutation and co-mutation vulnerabilities via deep learning

Pan

Akdemir

Gao

et al. 2023

Briefings in Bioinformatics

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Alzheimer’s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (‘AD-Syn-Net’), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.

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Deep learning based on multi-omics integration identifies potential therapeutic targets in breast cancer

Cited by 4 publications

References 71 publications

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

Recent Advancements for Detection and Prediction of Breast Cancer Using Deep Learning: A Review?

AD-Syn-Net: systematic identification of Alzheimer’s disease-associated mutation and co-mutation vulnerabilities via deep learning

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