Dataset Information

Prediction of enhancer-promoter interactions via natural language processing.

ABSTRACT: BACKGROUND:Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. RESULTS:We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~?0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~?0.940 can be achieved by combining sequence embedding features and experimental features. CONCLUSIONS:EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.

SUBMITTER: Zeng W

PROVIDER: S-EPMC5954283 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Prediction of enhancer-promoter interactions via natural language processing.

Zeng Wanwen W Wu Mengmeng M Jiang Rui R

BMC genomics 20180509 Suppl 2

<h4>Background</h4>Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput.<h4>Results</h4>We propose a novel computational framework EP2vec ...[more]

PMID: 29764360

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Project description:Highlights • What is the primary question addressed by this study? This paper explores the use of natural language processing techniques and machine learning models to predict loneliness in older community-dwelling adults.• What is the main finding of this study? There are structural differences in how older men and women talk about loneliness that can be detected using natural language processing techniques. Text features can be used to predict loneliness with reasonable validity.• What is the meaning of the finding? NLP and machine learning approaches provide a novel way to analyze text data to identify loneliness, while accounting for key sociodemographic factors like sex and age. Objective The growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults. Design Participants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared. Setting Independent living sector of a senior housing community in San Diego County. Participants Eighty English-speaking older adults with age range 66–94 (mean 83 years). Measurements Interviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions. Results Lonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity?=?0.90, specificity?=?1.00) and quantitative loneliness with 76% precision (sensitivity?=?0.57, specificity?=?0.89). Conclusions AI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.

Dataset Information

Prediction of enhancer-promoter interactions via natural language processing.

Publications

Prediction of enhancer-promoter interactions via natural language processing.

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