Dataset Information

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges.

ABSTRACT: Long noncoding RNAs (lncRNAs) make up a large proportion of transcriptome in eukaryotes, and have been revealed with many regulatory functions in various biological processes. When studying lncRNAs, the first step is to accurately and specifically distinguish them from the colossal transcriptome data with complicated composition, which contains mRNAs, lncRNAs, small RNAs and their primary transcripts. In the face of such a huge and progressively expanding transcriptome data, the in-silico approaches provide a practicable scheme for effectively and rapidly filtering out lncRNA targets, using machine learning and probability statistics. In this review, we mainly discussed the characteristics of algorithms and features on currently developed approaches. We also outlined the traits of some state-of-the-art tools for ease of operation. Finally, we pointed out the underlying challenges in lncRNA identification with the advent of new experimental data.

SUBMITTER: Li J

PROVIDER: S-EPMC7710504 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

The computational approaches of lncRNA identification based on coding potential: <i>Status quo</i> and challenges.

Li Jing J Zhang Xuan X Liu Changning C

Computational and structural biotechnology journal 20201119

Long noncoding RNAs (lncRNAs) make up a large proportion of transcriptome in eukaryotes, and have been revealed with many regulatory functions in various biological processes. When studying lncRNAs, the first step is to accurately and specifically distinguish them from the colossal transcriptome data with complicated composition, which contains mRNAs, lncRNAs, small RNAs and their primary transcripts. In the face of such a huge and progressively expanding transcriptome data, the <i>in-silico</i> ...[more]

PMID: 33304463

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Project description:By applying advanced health information technology to the health care field, health informatization helps optimize health resource allocation, improve health care services, and realize universal health coverage. COVID-19 has tested the status quo of China's health informatization, revealing challenges to the health care system. This viewpoint evaluates the development, status quo, and practice of China's health informatization, especially during COVID-19, and makes recommendations to address the health informatization challenges. We collected, assessed, and evaluated data on the development of China's health informatization from five perspectives-health information infrastructure, information technology (IT) applications, financial and intellectual investment, health resource allocation, and standard system-and discussed the status quo of the internet plus health care service pattern during COVID-19. The main data sources included China's policy documents and national plans on health informatization, commercial and public welfare sources and websites, public reports, institutional reports, and academic papers. In particular, we extracted data from the 2019 National Health Informatization Survey released by the National Health Commission in China. We found that China developed its health information infrastructure and IT applications, made significant financial and intellectual informatization investments, and improved health resource allocations. Tested during COVID-19, China's current health informatization system, especially the internet plus health care system, has played a crucial role in monitoring and controlling the pandemic and allocating medical resources. However, an uneven distribution of health resources and insufficient financial and intellectual investment continue to challenge China's health informatization. China's rapid development of health informatization played a crucial role during COVID-19, providing a reference point for global pandemic prevention and control. To further promote health informatization, China's health informatization needs to strengthen top-level design, increase investment and training, upgrade the health infrastructure and IT applications, and improve internet plus health care services.

Dataset Information

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges.

Publications

The computational approaches of lncRNA identification based on coding potential: <i>Status quo</i> and challenges.

Similar Datasets

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