Applications of deep learning in understanding gene regulation

Li, Zhongxiao; Gao, Elva; Zhou, Juexiao; Han, Wenkai; Xu, Xiaopeng; Gao, Xin

doi:10.1016/j.crmeth.2022.100384

Cited by 22 publications

(12 citation statements)

References 200 publications

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“…While some methods take into account TF binding profiles at the gene promoter region or ChIP-seq histone modification, others predict gene expression only based on the DNA sequence. By developing a network model, more advanced techniques try to jointly model the expression of every gene in a cell [66].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Sessa,

Esposito,

Cocimano

et al. 2024

Applied Sciences

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The term artificial intelligence (AI) was coined in the 1950s and it has successfully made its way into different fields of medicine. Forensic sciences and AI are increasingly intersecting fields that hold tremendous potential for solving complex criminal investigations. Considering the great evolution in the technologies applied to forensic genetics, this literature review aims to explore the existing body of research that investigates the application of AI in the field of forensic genetics. Scopus and Web of Science were searched: after an accurate evaluation, 12 articles were included in the present systematic review. The application of AI in the field of forensic genetics has predominantly focused on two aspects. Firstly, several studies have investigated the use of AI in haplogroup analysis to enhance and expedite the classification process of DNA samples. Secondly, other research groups have utilized AI to analyze short tandem repeat (STR) profiles, thereby minimizing the risk of misinterpretation. While AI has proven to be highly useful in forensic genetics, further improvements are needed before using these applications in real cases. The main challenge lies in the communication gap between forensic experts: as AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Sessa,

Esposito,

Cocimano

et al. 2024

Applied Sciences

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“…Commonly used machine learning models include XGBoost models which have previously been shown to have high predictive capabilities in modeling gene expression values [38], as well as genetic and metabolic networks [39]. Complementary to this, DeepLearning algorithms have been used in a plethora of bioengineering applications notably to predict guide-RNA activity for CRISPR/Cas-based genome engineering [40] and gene regulation [41]. Additionally, stacked ensembles, distributed random forest (DRF), general linear models (GLMs), and gradient booster models (GBM) models have also found applications in various biological areas [42,43].…”

Section: Teemi For Design-build-test-learn Cycle Imentioning

confidence: 99%

teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Petersen,

Levassor,

Pedersen

et al. 2024

PLoS Comput Biol

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Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

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“…Sequence‐based DL models have increasingly been used for analyzing and predicting various molecular features in single‐cell data, including chromatin accessibility and gene expression. [ 265 ] For example, Basset [ 266 ] and DeepFlyBrian [ 203 ] have been employed to predict chromatin accessibility from DNA sequence information at the pseudobulk level. [ 25a,203 ] Similarly, other deep learning models have been developed for analyzing scATAC‐seq data, which provides information about chromatin accessibility at single‐cell resolution.…”

Section: Deep Learning Application In a Single‐cell Atlasmentioning

confidence: 99%

Mapping Cell Atlases at the Single‐Cell Level

Ye,

Wang,

et al. 2023

Advanced Science

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Recent advancements in single‐cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single‐cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

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Applications of deep learning in understanding gene regulation

Cited by 22 publications

References 200 publications

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Mapping Cell Atlases at the Single‐Cell Level

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