Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence

Chen, Li; Wang, Ye

doi:10.1093/bioinformatics/btac214

Cited by 15 publications

(11 citation statements)

References 51 publications

(42 reference statements)

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“…Therefore, transfer learning can leverage both labelled data from source task, which usually has a large sample size, and labelled data from the target task with a small sample size to improve the prediction performance for the target task. Particularly, deep transfer learning, which utilizes deep neural network in the transfer learning, has been widely adopted in genomic study ( Chen et al , 2022 ). Practically, the first layers are pretrained using labelled data from the source task, frozen and transferred to the target task, where the last layers or all layers are fine-tuned using the labelled data from the target task.…”

Section: Methodsmentioning

confidence: 99%

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach

Agarwal

Chen

2022

Bioinformatics

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Motivation Promoter-centered chromatin interactions, which include promoter-enhancer and promoter-promoter interactions, are important to decipher gene regulation and disease mechanisms. The development of next generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues/cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Most existing computational methods for predicting chromatin interactions are based on in situ Hi-C and can detect chromatin interactions across the entire genome. However, they may not be optimal for predicting promoter-centered chromatin interactions. Results We develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific promoter-enhancer and promoter-promoter interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting promoter-enhancer interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions. Availability https://github.com/lichen-lab/DeepPHiC Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach

Agarwal

Chen

2022

Bioinformatics

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“…Methods such as Mendelian randomization are providing new insights into causal pathways for risk factors [ 102 ], but can be inadequately powered and suffer from weak instrument and survival bias. Machine learning methods, such as deep learning approaches, could improve the way risk variants are identified and functionally assessed within genome-wide association studies [ 103 – 105 ], creating stronger genetic instruments to improve causal analysis. Contemporary causal machine learning approaches have the potential to enhance our understanding of the underlying mechanisms which connect multiple risk factors, pathologies and the clinical syndrome of dementia itself.…”

Section: Preventionmentioning

confidence: 99%

Harnessing the potential of machine learning and artificial intelligence for dementia research

et al. 2023

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Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

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“…Therefore, methods pinpointing to such regulatory SNPs (rSNPs) are a topic of current interest. Several methods exist that successfully highlight rSNPs based on epigenetic information like open chromatin data, TF and histone ChIP-seq without taking into account which TF might be affected [30,25,2,12]. In contrast, methods that evaluate the effect of a SNP on a TFBS rely on the ability to describe the binding behaviour of a Transcription Factor (TF) to assess the difference induced by a non-coding SNP.…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to identify regulatory DNA variations

Baumgarten

Rumpf

Kessler

et al. 2023

Preprint

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Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data. obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data. Results: We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes. Conclusions: Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.

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Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence

Cited by 15 publications

References 51 publications

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach

DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach

Harnessing the potential of machine learning and artificial intelligence for dementia research

A statistical approach to identify regulatory DNA variations

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