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Literature DB >> 33925407

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.

Marco Del Giudice^1,2, Serena Peirone^1,3, Sarah Perrone^1,4, Francesca Priante^1,4, Fabiola Varese^1,5, Elisa Tirtei⁶, Franca Fagioli^6,7, Matteo Cereda^1,2.

Abstract

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

Entities: Chemical Disease Gene Species

Keywords: RNA sequencing; artificial intelligence; cancer heterogeneity

Year: 2021 PMID： 33925407 DOI： 10.3390/ijms22094563

Source DB: PubMed Journal: Int J Mol Sci ISSN： 1422-0067 Impact factor: 5.923

97 in total

1. Predicting HLA class II antigen presentation through integrated deep learning.

Authors: Binbin Chen; Michael S Khodadoust; Niclas Olsson; Lisa E Wagar; Ethan Fast; Chih Long Liu; Yagmur Muftuoglu; Brian J Sworder; Maximilian Diehn; Ronald Levy; Mark M Davis; Joshua E Elias; Russ B Altman; Ash A Alizadeh
Journal: Nat Biotechnol Date: 2019-10-14 Impact factor: 54.908

Review 2. A primer on deep learning in genomics.

Authors: James Zou; Mikael Huss; Abubakar Abid; Pejman Mohammadi; Ali Torkamani; Amalio Telenti
Journal: Nat Genet Date: 2018-11-26 Impact factor: 38.330

3. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

Authors: Julien Racle; Kaat de Jonge; Petra Baumgaertner; Daniel E Speiser; David Gfeller
Journal: Elife Date: 2017-11-13 Impact factor: 8.140

4. Determining cell type abundance and expression from bulk tissues with digital cytometry.

Authors: Aaron M Newman; Chloé B Steen; Chih Long Liu; Andrew J Gentles; Aadel A Chaudhuri; Florian Scherer; Michael S Khodadoust; Mohammad S Esfahani; Bogdan A Luca; David Steiner; Maximilian Diehn; Ash A Alizadeh
Journal: Nat Biotechnol Date: 2019-05-06 Impact factor: 54.908

5. CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

Authors: Yue Zhao; Ziwei Pan; Sandeep Namburi; Andrew Pattison; Atara Posner; Shiva Balachander; Carolyn A Paisie; Honey V Reddi; Jens Rueter; Anthony J Gill; Stephen Fox; Kanwal P S Raghav; William F Flynn; Richard W Tothill; Sheng Li; R Krishna Murthy Karuturi; Joshy George
Journal: EBioMedicine Date: 2020-10-09 Impact factor: 8.143

6. A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients.

Authors: Xiaoqiang Zhu; Xianglong Tian; Linhua Ji; Xinyu Zhang; Yingying Cao; Chaoqin Shen; Ye Hu; Jason W H Wong; Jing-Yuan Fang; Jie Hong; Haoyan Chen
Journal: NPJ Precis Oncol Date: 2021-02-12

3. Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data.

Authors: Naiyar Iqbal; Pradeep Kumar
Journal: Comput Biol Med Date: 2022-06-03 Impact factor: 6.698

3 in total

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.

1. Predicting HLA class II antigen presentation through integrated deep learning.

Review 2. A primer on deep learning in genomics.

3. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

4. Determining cell type abundance and expression from bulk tissues with digital cytometry.

5. CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

6. A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients.

7. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

8. DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data.

9. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

10. Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.

Review 1. The Architecture of a Precision Oncology Platform.

2. Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".

3. Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data.