Gene expression inference with deep learning

By Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian, Xiaohui S. Xie. 2015

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Daniel Quang
2. Brett K. Beaulieu-Jones
3. Yifan Peng
4. Xiaohui S. Xie
5. Anshul B Kundaje
6. Avanti Shrikumar
7. Casey S. Greene
8. Amr M. Alexandari

In Biophysics

1. Oren Z. Kraus
2. Anne E Carpenter
3. Casey S. Greene
4. G. Way
5. Charles Boone
6. Brenda J. Andrews
7. Ben T Grys
8. Nil Sahin

In Molecular Biology

1. Casey S. Greene
2. Daniel Quang
3. Xiaohui S. Xie
4. Amir Hussain
5. Mufti Mahmud
6. M. S. Kaiser
7. Yifan Peng
8. Brett K. Beaulieu-Jones

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[146] inferred the expression level of all genes with a single neural network, but due to memory restrictions they randomly partitioned genes into two separately analyzed halves.”
• FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “They have been successfully applied to predicting splicing patterns [18], predicting variant deleteriousness [19], and gene expression inference [20].”
• Deep learning in bioinformatics (2016, methodology): “[96] applied MLP to both microarray and RNA-seq expression data to infer expression of up to 21 000 target genes from only 1000 landmark genes.”
• Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “Deep learning has been applied to numerous types of biological data for modeling gene expression (Chen et al., 2016a,b) and predicting protein structure (Zhou and Troyanskaya, 2015), DNA methylation (Wang et al., 2016), and protein–nucleic acid interactions (Alipanahi et al., 2015).”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “exploited a DNN based method for GE profiling on RNA-seq and microarray-based GE Omnibus dataset [83].”
• Deep Learning and Its Applications in Biomedicine (2018, background): “…genes from the NIH Integrated Network-based Cellular Signatures (LINCS) program, Chen et al. presented D-GEX, a deep learning method with dropout as regularization, which significantly outperformed linear regression (LR) in terms of prediction accuracy on both microarray and RNA-seq data [145].”
• Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware (2019, methodology): “(Chen et al., 2016) have presented MLPs for gene expression inference (D-GEX) to perform gene expression inference to the GEO microarray data and RNA-seq expression data.”
• Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, background): “There have been studies that have shown some success with various -omics data, including prediction of expression (Chen et al., 2016), prediction of drug response in cell lines (Sakellaropoulos et al., 2019), prediction of tissue-of-origin and cancer type (Sakellaropoulos et al., 2019), and…”
• Gene Expression Value Prediction Based on XGBoost Algorithm (2019, methodology): “Inspired by this, many scholars have suggested that the expression value of the landmark gene can be used to predict the expression value of the target gene, which will greatly reduce the cost of the gene expression profiling (Chen et al., 2016).”