Network embedding in biomedical data science

Su, Chang; Tong, Jie; Zhu, Yongjun; Cui, Peng; Wang, Fei

doi:10.1093/bib/bby117

Cited by 135 publications

(91 citation statements)

References 68 publications

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“…In addition, gene-gene interactions are also important genomic domain knowledge which may help genetic data modeling and can be downloaded from existing public databases, such as KEGG 81 and BioGRID 82 . To handle injection of domain knowledge, powerful techniques have been extensively developed, such as kernel 68 and knowledge embedding 83 , 84 approaches.…”

Section: Discussion: Limitations and Future Directionsmentioning

confidence: 99%

Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease

Tong

Wang

2020

npj Parkinsons Dis.

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High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.

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Section: Discussion: Limitations and Future Directionsmentioning

confidence: 99%

Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease

Tong

Wang

2020

npj Parkinsons Dis.

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“…Another important direction is to incorporate domain knowledge. The existing biomedical knowledge bases are invaluable sources for solving healthcare problems 133,134 . Incorporating domain knowledge could address the limitation of data volume, problems of data quality, as well as model generalizability.…”

Section: Domain Knowledgementioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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“…There have been numerous studies on ADR prediction in pre-marketing phases, attempting graph-based approaches on biomedical information sources [12,15,18,22]. These studies predicted potential side-effects of drug candidate molecules based on their chemical structures [15] and additional biological properties [12].…”

Section: Related Workmentioning

confidence: 99%

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Kwak

Lee

Yoon

et al. 2020

Lecture Notes in Computer Science

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Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than premarketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graphbased framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.

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Network embedding in biomedical data science

Cited by 135 publications

References 68 publications

Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease

Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease

Deep learning in mental health outcome research: a scoping review

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

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