Analysis of super-enhancer using machine learning and its application to medical biology

Hamamoto, Ryuji; Takasawa, Ken; Shinkai, Norio; Machino, Hidenori; Kouno, Nobuji; Asada, Ken; Komatsu, Masaaki; Kaneko, Syuzo

doi:10.1093/bib/bbad107

Cited by 5 publications

(2 citation statements)

References 152 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised learning algorithms are trained on unlabeled data to find patterns and relationships in the data. ML algorithms can answer various biological questions, such as predicting disease risk, identifying new disease genes, grouping genes with similar expression patterns or identifying groups of patients with similar clinical features [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning

KafiKang,

Abeysiriwardana,

Singh

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in turkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types of turkey reoviruses and identify novel emerging variants. Additionally, it focuses on classifying variants of turkey reoviruses by leveraging various machine learning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, and deep learning algorithms, including convolutional neural networks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods. The results indicate that machine learning methods achieve an average accuracy of 92%, F1-Macro of 93% and F1-Weighted of 92% scores in classifying reovirus types. In contrast, the CNN model demonstrates an average accuracy of 85%, F1-Macro of 71% and F1-Weighted of 84% scores in the same classification task. The superior performance of the machine learning classifiers provides valuable insights into reovirus evolution and mutation, aiding in detecting emerging variants of pathogenic TARVs and THRVs.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning

KafiKang,

Abeysiriwardana,

Singh

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Additionally, increased chromatin accessibility has been identified within these regions. Abnormalities in the function of super-enhancers have been reported to be associated with cancer, type 1 diabetes, and Alzheimer’s disease [ 10 , 11 ]. Particularly in cancer, super-enhancers may play a crucial role in the dysregulation of gene expression.…”

Section: Introductionmentioning

confidence: 99%

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations

Kaneko,

Takasawa,

Asada

et al. 2024

Mol Cancer

View full text Add to dashboard Cite

Background In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. Methods We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. Results We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. Conclusions Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

show abstract

An in-depth analysis and exploreation with focus on the biofilm inStaphylococcus aureus

Zhang,

Chen,

Pan

et al. 2024

Preprint

View full text Add to dashboard Cite

Research into the biofilm formation inStaphylococcus aureushas benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The accumulation of these data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in the biofilm formation ofStaphylococcus aureusthrough machine learning strategies and co-expression analyses. Herein, we harnessed all available RNA sequencing data related toStaphylococcus aureusbiofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state via employing machine learning and differential expression analyses. By weighted gene co-expression analysis and our previously developed predictor, important functional modules, potential biofilm-associated proteins and subnetwork of biofilm formation pathway were found. By constructing a protein-protein interaction (PPI) network, we discovered several hitherto unreported novel protein interactions within these functional modules. To make these data more straightforward to experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/). IMPORTANCE In this work, we conducted a comprehensive and in-depth exploration of RNA sequencing data in biofilm research through differential expression analysis, machine learning, WGCNA, and biofilm-associated protein predictive analysis, which has also illuminated novel analytical perspective for other research into bacterial phenotypes. And, to provide researchers with unimpeded access to these data, we developed a database name SAdb for the storage and analysis ofStaphylococcus aureusomics data. We believe that this study will captivate the interest of researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.

show abstract

Analysis of super-enhancer using machine learning and its application to medical biology

Cited by 5 publications

References 152 publications

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations

An in-depth analysis and exploreation with focus on the biofilm inStaphylococcus aureus

Contact Info

Product

Resources

About