Learning functional embedding of genes governed by pair-wised labels

Cao, Jingjun; Wu, Zhengli; Ye, Wenting; Wang, Haohan

doi:10.1109/ciapp.2017.8167247

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another thread for utilizing deep learning to characterize gene expression is to describe the pairwise relationship. Wang et al [138] showed that CNN can be seen as an effective replacement for the frequently used Pearson correlation applied to pairs of genes; therefore, they built a multitask CNN that can consider the information of GO semantics and interaction between genes together to extract higher-level representations of gene pairs for the further classification task, which is further extended by two shared-parameter networks [157]. Recently, LLMs have come into play for such a pairwise relationship: Cui et al [158] introduced a GPT-based foundational model and found a positive pairwise correlation between the similarity of the gene embeddings and the number of common pathways shared by these genes; similarly, Yang et al [159] utilized the attention weights in transformers to reflect the contribution of each gene and the interaction of gene pairs.…”

Section: Gene Expression Characterizationmentioning

confidence: 99%

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

Yue,

Wang,

Zhang

et al. 2023

IJMS

View full text Add to dashboard Cite

The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics.

show abstract

Section: Gene Expression Characterizationmentioning

confidence: 99%

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

Yue,

Wang,

Zhang

et al. 2023

IJMS

View full text Add to dashboard Cite

show abstract

“…In RFs, each tree in the ensemble is built from a sample drawn with replacement (i.e., a bootstrap sample) from the training set. The Gaussian naïve Bayes classifier was used in this study as an evaluator of a feature subset (Cao et al, 2017). GaussianNB implements the Gaussian Naive Bayes algorithm for classification.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

et al. 2020

View full text Add to dashboard Cite

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

show abstract

“…Another thread for utilizing deep learning to characterize gene expression is to describe the pairwise relationship. Wang et al (2017b) showed that CNN can be seen as an effective replacement for the frequently used Pearson correlation applied to pair of genes, therefore they built a multi-task CNN that can consider the information of GO semantics and interaction between genes together to extract higher level representations of gene pairs for further classification task, which is further extended by two shared-parameter networks (Cao et al, 2017a).…”

Section: Gene Expression Characterizationmentioning

confidence: 99%

Deep Learning for Genomics: A Concise Overview

Yue¹,

Wang²

2018

Preprint

Self Cite

View full text Add to dashboard Cite

This data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out current challenges and potential research directions for future genomics applications.

show abstract

Learning functional embedding of genes governed by pair-wised labels

Cited by 5 publications

References 9 publications

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Deep Learning for Genomics: A Concise Overview

Contact Info

Product

Resources

About