Volume 23 Supplement 9

Proceedings of the 15th International Conference on Data and Text Mining in Biomedical Informatics (DTMBIO 2021)

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 Editors declare that they have no competing interests.

Virtual22 October 2021

Conference website

Edited by Sunyong Yoo, Junho Kim and Sunjae Lee.

A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists

G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related dise...

Authors: Jooseong Oh, Hyi-thaek Ceong, Dokyun Na and Chungoo Park

Citation: BMC Bioinformatics 2022 23(Suppl 9):346

Content type: Research Published on: 18 August 2022
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Avoiding background knowledge: literature based discovery from important information

Automatic literature based discovery attempts to uncover new knowledge by connecting existing facts: information extracted from existing publications in the form of ...

Authors: Judita Preiss

Citation: BMC Bioinformatics 2023 23(Suppl 9):570

Content type: Research Published on: 14 March 2023
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Correction: Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients

Authors: Seungwon Oh, Sae-Ryung Kang, In-Jae Oh and Min-Soo Kim

Citation: BMC Bioinformatics 2023 23(Suppl 9):573

Content type: Correction Published on: 1 June 2023

The original article was published in BMC Bioinformatics 2023 24:39
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Proceedings of the 15th International Conference on Data and Text Mining in Biomedical Informatics (DTMBIO 2021)

Research

A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists

Avoiding background knowledge: literature based discovery from important information

Correction: Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients

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