MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

Williams, Justin; Xu, Beisi; Putnam, Daniel K.; Thrasher, Andrew; Li, Chunliang; Yang, Jun; Chen, Xiang

doi:10.1186/s13059-020-02220-y

Cited by 10 publications

(1 citation statement)

References 80 publications

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“…It is challenging to develop a testing strategy that can distinguish between normal epigenetic regulation following chemical exposure, as opposed to adverse epigenetic reprograming causally linked to carcinogenesis. For example, enrichment of H3K27ac in the promoter region of induced genes is a normal process [183], and not indicative of adversity even when detected following chemical exposure. In contrast, epigenetic changes associated with a carcinogenic key event can provide mechanistic understanding of adversity.…”

Section: Adverse Epigenetic Effects Vs Normal Epigenetic Regulation and Disturbancesmentioning

confidence: 99%

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Desaulniers

Vasseur

Jacobs

et al. 2021

IJMS

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Epigenetics involves a series of mechanisms that entail histone and DNA covalent modifications and non-coding RNAs, and that collectively contribute to programing cell functions and differentiation. Epigenetic anomalies and DNA mutations are co-drivers of cellular dysfunctions, including carcinogenesis. Alterations of the epigenetic system occur in cancers whether the initial carcinogenic events are from genotoxic (GTxC) or non-genotoxic (NGTxC) carcinogens. NGTxC are not inherently DNA reactive, they do not have a unifying mode of action and as yet there are no regulatory test guidelines addressing mechanisms of NGTxC. To fil this gap, the Test Guideline Programme of the Organisation for Economic Cooperation and Development is developing a framework for an integrated approach for the testing and assessment (IATA) of NGTxC and is considering assays that address key events of cancer hallmarks. Here, with the intent of better understanding the applicability of epigenetic assays in chemical carcinogenicity assessment, we focus on DNA methylation and histone modifications and review: (1) epigenetic mechanisms contributing to carcinogenesis, (2) epigenetic mechanisms altered following exposure to arsenic, nickel, or phenobarbital in order to identify common carcinogen-specific mechanisms, (3) characteristics of a series of epigenetic assay types, and (4) epigenetic assay validation needs in the context of chemical hazard assessment. As a key component of numerous NGTxC mechanisms of action, epigenetic assays included in IATA assay combinations can contribute to improved chemical carcinogen identification for the better protection of public health.

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Section: Adverse Epigenetic Effects Vs Normal Epigenetic Regulation and Disturbancesmentioning

confidence: 99%

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Desaulniers

Vasseur

Jacobs

et al. 2021

IJMS

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Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks

et al. 2023

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For gas separation and catalysis by metal-organic frameworks (MOFs), gas diffusion has a substantial impact on the process' overall rate, so it is necessary to determine the molecular diffusion behavior within the MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting machine (LGBM), is trained to predict the molecular diffusivity and selectivity of 9 gases (Kr, Xe, CH 4 , N 2 , H 2 S, O 2 , CO 2 , H 2 , and He). For these 9 gases, LGBM displays high accuracy (average R 2 = 0.962) and superior extrapolation for the diffusivity of C 2 H 6 . And this model calculation is five orders of magnitude faster than molecular dynamics (MD) simulations. Subsequently, using the trained LGBM model, an interactive desktop application is developed that can help researchers quickly and accurately calculate the diffusion of molecules in porous crystal materials. Finally, the authors find the difference in the molecular polarizability (𝚫Pol) is the key factor governing the diffusion selectivity by combining the trained LGBM model with the Shapley additive explanation (SHAP). By the calculation of interpretable ML, the optimal MOFs are selected for separating binary gas mixtures and CO 2 methanation. This work provides a new direction for exploring the structure-property relationships of MOFs and realizing the rapid calculation of molecular diffusivity.

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A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors

Jones

Putnam

Williams

et al. 2023

Methods in Molecular Biology

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MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

Cited by 10 publications

References 80 publications

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Interpretable Machine‐Learning and Big Data Mining to Predict Gas Diffusivity in Metal‐Organic Frameworks

A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors

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