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

Nguyen, Thi Mai; Kim, Nackhyoung; Kim, Da Hae; Le, Hoang Long; Piran, Md. Jalil; Um, Soo‐Jong; Kim, Jin Hee

doi:10.3390/biomedicines9111733

Cited by 10 publications

(7 citation statements)

References 90 publications

(273 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several N-glycan signatures, such as sialylation, fucosylation, bisecting GlcNAcylation, and branching, are regulated by various glycosyltransferase activities, and their synthetic pathways could influence each other. Although discriminating three or more diseases by discriminant analysis using N-glycan signatures has been limited, an ML approach combined with omics data has been used for early detection of diseases, including cancer [16][17][18][19] and seems to be suitable for extraction of disease-specific N-glycan features and precise discrimination between benign and malignant conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, ML approaches could be an important tool for these analyses. [16][17][18][19] We aimed to simultaneously detect nine urological diseases including five cancers (RCC, BCa, UTUC, PC, and GCT) and three benign diseases (BPH, US, and UTI) using a diagnostic modeling ML approach with Ig N-glycan signature data. Thirty-seven patients were excluded because the presence or absence of disease could not be determined from medical records.…”

Section: Introductionmentioning

confidence: 99%

“…Statistical analyses to extract disease‐specific N ‐glycan signatures from vast amounts of N ‐glycomics data on complex N ‐glycan structures and their synthetic pathways are limited. Therefore, ML approaches could be an important tool for these analyses 16–19 . We aimed to simultaneously detect nine urological diseases including five cancers (RCC, BCa, UTUC, PC, and GCT) and three benign diseases (BPH, US, and UTI) using a diagnostic modeling ML approach with Ig N ‐glycan signature data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning diagnosis by immunoglobulinN‐glycan signatures for precision diagnosis of urological diseases

et al. 2022

View full text Add to dashboard Cite

Early diagnosis of urological diseases is often difficult due to the lack of specific biomarkers. More powerful and less invasive biomarkers that can be used simultaneously to identify urological diseases could improve patient outcomes. The aim of this study was to evaluate a urological disease-specific scoring system established with a machine learning (ML) approach using Ig N-glycan signatures. Immunoglobulin Nglycan signatures were analyzed by capillary electrophoresis from 1312 serum subjects with hormone-sensitive prostate cancer (n = 234), castration-resistant prostate cancer (n = 94), renal cell carcinoma (n = 100), upper urinary tract urothelial cancer (n = 105), bladder cancer (n = 176), germ cell tumors (n = 73), benign prostatic hyperplasia (n = 95), urosepsis (n = 145), and urinary tract infection (n = 21) as well as healthy volunteers (n = 269). Immunoglobulin N-glycan signature data were used in a supervised-ML model to establish a scoring system that gave the probability of the This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning diagnosis by immunoglobulinN‐glycan signatures for precision diagnosis of urological diseases

et al. 2022

View full text Add to dashboard Cite

show abstract

“…RNN are a subclass of neural networks that introduce recurrent connections between the neuron units, which furnish the network with a memory capability: past observations can be employed to understand the current observation or predict future observations in an input sequence. These characteristics provide RNN with sequential dynamic behavior, making them suitable for processing sequential data and identifying inner relationships and variation tendencies [101,102]. Sahin et al [103] developed an RNN framework to model a stability mechanism for robust feature selection of microarray datasets.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

View full text Add to dashboard Cite

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

show abstract

“…Most of these methods are based on networks (34,35). These computational methods have been widely used in the discovery of diseaserelated genes (33,(35)(36)(37)(38)(39), genetic mechanism (40,41), gene expression (37,40), protein function (42,43), metabolic association (44,45), and drug target (46,47). Therefore, in this paper, we developed a novel method named "DBN-GTN" to identify gastric cancer-related genes in a large scale.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Gastric Cancer-Related Genes Based on the Graph Transformer Network

Chen

Sun²,

Yang³

2022

Front. Oncol.

View full text Add to dashboard Cite

Gastric cancer is a complex multifactorial and multistage process that involves a large number of tumor-related gene structural changes and abnormal expression. Therefore, knowing the related genes of gastric cancer can further understand the pathogenesis of gastric cancer and provide guidance for the development of targeted drugs. Traditional methods to discover gastric cancer-related genes based on biological experiments are time-consuming and expensive. In recent years, a large number of computational methods have been developed to identify gastric cancer-related genes. In addition, a large number of experiments show that establishing a biological network to identify disease-related genes has higher accuracy than ordinary methods. However, most of the current computing methods focus on the processing of homogeneous networks, and do not have the ability to encode heterogeneous networks. In this paper, we built a heterogeneous network using a disease similarity network and a gene interaction network. We implemented the graph transformer network (GTN) to encode this heterogeneous network. Meanwhile, the deep belief network (DBN) was applied to reduce the dimension of features. We call this method “DBN-GTN”, and it performed best among four traditional methods and five similar methods.

show abstract

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

Cited by 10 publications

References 90 publications

Machine learning diagnosis by immunoglobulinN‐glycan signatures for precision diagnosis of urological diseases

Machine learning diagnosis by immunoglobulinN‐glycan signatures for precision diagnosis of urological diseases

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Prediction of Gastric Cancer-Related Genes Based on the Graph Transformer Network

Contact Info

Product

Resources

About