Volume 23 Supplement 8

Selected articles from the 16th International Symposium on Bioinformatics Research and Applications (ISBRA-20): bioinformatics

Research

Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editor declares that they have no competing interests.

Virtual1-4 December 2020

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Edited by Murray Patterson.

Identifying essential proteins from protein–protein interaction networks based on influence maximization

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, ...

Authors: Weixia Xu, Yunfeng Dong, Jihong Guan and Shuigeng Zhou

Citation: BMC Bioinformatics 2022 23(Suppl 8):339

Content type: Research Published on: 16 August 2022
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Statistical evidence for the presence of trajectory in single-cell data

Cells progressing from an early state to a developed state give rise to lineages in cell differentiation. Knowledge of these lineages is central to developmental biology. Each biological lineage corresponds to...

Authors: Lovemore Tenha and Mingzhou Song

Citation: BMC Bioinformatics 2022 23(Suppl 8):340

Content type: Research Published on: 16 August 2022
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Visualizing the knowledge structure and evolution of bioinformatics

Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did...

Authors: Jiaqi Wang, Zeyu Li and Jiawan Zhang

Citation: BMC Bioinformatics 2022 23(Suppl 8):404

Content type: Research Published on: 30 September 2022
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IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations

RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondar...

Authors: Alexander Churkin, Yann Ponty and Danny Barash

Citation: BMC Bioinformatics 2022 23(Suppl 8):424

Content type: Methodology Published on: 14 October 2022
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A risk factor attention-based model for cardiovascular disease prediction

Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient’s electronic medical record (EMR) to predict CVD automatically...

Authors: Yanlong Qiu, Wei Wang, Chengkun Wu and Zhichang Zhang

Citation: BMC Bioinformatics 2022 23(Suppl 8):425

Content type: Research Published on: 14 October 2022
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Drug response prediction using graph representation learning and Laplacian feature selection

Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic ...

Authors: Minzhu Xie, Xiaowen Lei, Jianchen Zhong, Jianxing Ouyang and Guijing Li

Citation: BMC Bioinformatics 2022 23(Suppl 8):532

Content type: Methodology Published on: 9 December 2022
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Detecting genomic deletions from high-throughput sequence data with unsupervised learning

Authors: Xin Li and Yufeng Wu

Citation: BMC Bioinformatics 2023 23(Suppl 8):568

Content type: Methodology Published on: 27 January 2023
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Selected articles from the 16th International Symposium on Bioinformatics Research and Applications (ISBRA-20): bioinformatics

Research

Identifying essential proteins from protein–protein interaction networks based on influence maximization

Statistical evidence for the presence of trajectory in single-cell data

Visualizing the knowledge structure and evolution of bioinformatics

IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations

A risk factor attention-based model for cardiovascular disease prediction

Drug response prediction using graph representation learning and Laplacian feature selection

Detecting genomic deletions from high-throughput sequence data with unsupervised learning

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