DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features

Yao, Zhaomin; Li, Fei; Xie, Weiming; Chen, Jiaming; Wu, Jiezhang; Zhan, Ying; Wu, Xiaodan; Wang, Zhiguo; Zhang, Guoxu

doi:10.1016/j.compbiomed.2024.108166

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…Then, one hot TL method was applied to identify 4-methylcytosine in each species and achieved an accuracy of 86.1–90.7%, sensitivity of 88–92.5, and specificity of 84.2–88.8%. The performance of the DeepSF-4mC was lowest in A. thaliana and the highest in C. elegans and still outperformed similar studies that did not use TL [ 89 ].…”

Section: Gene Expressionmentioning

confidence: 64%

“…After training, they fine-tuned the model to identify the desired methyl nucleotide in a particular species. The framework of this method is similar to that of the previous studies by Zhuang et al (2019), Jing et al (2021), and Yao et al (2024) [ 75 , 77 , 78 , 89 ]: training a model on all of the different data and fine-tuning for each particular ( Figure 2 presents the abstract of this method). The advantage of using this method is that the source and target domains are relatively similar.…”

Section: Gene Expressionmentioning

confidence: 90%

“…4-methylcytosine is another important epigenetic change in human DNA. Yao et al (2024) [ 89 ] proposed DeepSF-4mC, an AI model to predict the 4-methylcytosine sites in DNA. They trained the model on DNA sequence data of three species: A. thaliana, Caenorhabditis elegans, and Drosophila melanogaster.…”

Section: Gene Expressionmentioning

confidence: 99%

“…Then, the AI was fine-tuned on the remaining data (target domain) (e.g., AI was fine-tuned on DNA sequences of a particular species). This method was used by [ 75 , 77 , 78 , 81 , 89 ], and is demonstrated in Figure 2 . Compared with the first method, the second method increases the similarities between source and target domains.…”

Section: Limitations and Challengesmentioning

confidence: 99%

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Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Ashayeri,

Sobhi,

Pławiak

et al. 2024

Cancers

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Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

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Section: Gene Expressionmentioning

confidence: 64%

Section: Gene Expressionmentioning

confidence: 90%

Section: Gene Expressionmentioning

confidence: 99%

Section: Limitations and Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Ashayeri,

Sobhi,

Pławiak

et al. 2024

Cancers

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show abstract

“… 3 , 4 Despite diverse treatment modalities, advanced KIRC exhibits marked resistance to conventional chemotherapy and radiotherapy. 5 , 6 , 7 Recent advancements in bioinformatics have catalysed the emergence of precision medicine, affording a more nuanced foundation for personalized treatment approaches. 8 Consequently, a comprehensive comprehension of the pathophysiological mechanisms underlying renal clear cell carcinoma, coupled with the identification of biomarkers and their integration with effective immunotherapeutic strategies, constitutes pivotal facets of contemporary research.…”

Section: Introductionmentioning

confidence: 99%

Unravelling infiltrating T‐cell heterogeneity in kidney renal clear cell carcinoma: Integrative single‐cell and spatial transcriptomic profiling

Chen,

Zuo,

Huang

et al. 2024

J Cellular Molecular Medi

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Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning assays, wound‐healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid‐ to late‐stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG‐based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T‐cell heterogeneity in KIRC using single‐cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

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Intelligence algorithm for the treatment of gastrointestinal diseases based on immune monitoring and neuroscience: A revolutionary tool for translational medicine

Li,

Qin,

Wang

et al. 2025

Alexandria Engineering Journal

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DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features

Cited by 5 publications

References 38 publications

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Unravelling infiltrating T‐cell heterogeneity in kidney renal clear cell carcinoma: Integrative single‐cell and spatial transcriptomic profiling

Intelligence algorithm for the treatment of gastrointestinal diseases based on immune monitoring and neuroscience: A revolutionary tool for translational medicine

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