LiqBioer: a manually curated database of cancer biomarkers in body fluid

Geng, Yiding; Jin, Lü; Tang, Guangjue; Zhao, Zhangxiang; Gu, Yunyan; Yang, Dan

doi:10.1093/database/baac077

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…This is reflected in a persistent growth in the number of related scientific publications (journal articles and patents) in the recent decades (Figure A). A number of compendiums such as GOBIOM, GLOBOCAN 2020, OncoMX, , MarkerDB, , LiqBioer, , BIONDA, , BiomarkerBase, The Human Gene Mutation Database, Cancer Biomarkers database, CBD: a biomarker database for colorectal cancer, ONS Biomarker Database, and others have collected hundreds of proposed biomarker candidates. However, even though a large number of biomarker candidates have been proposed, very few have progressed to the stage of clinical validation along their clinical trial pathway. − …”

Section: Biomarker Research Growthmentioning

confidence: 99%

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Tenchov,

Sapra,

Sasso

et al. 2024

ACS Pharmacol. Transl. Sci.

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Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

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Section: Biomarker Research Growthmentioning

confidence: 99%

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Tenchov,

Sapra,

Sasso

et al. 2024

ACS Pharmacol. Transl. Sci.

View full text Add to dashboard Cite

show abstract

“…This is reflected in a persistent growth in the number of related scientific publications (journal articles and patents) in the recent decades (Figure 3A). A number of compendiums such as GOBIOM 84 , GLOBOCAN 2020 85 , OncoMX 86, 87 , MarkerDB 88, 89 , LiqBioer 90,91 , BIONDA 92,93 , BiomarkerBase 94 , The Human Gene Mutation Database 95 , Cancer Biomarkers database 96 , CBD: a biomarker database for colorectal cancer 97 , ONS Biomarker Database 98 , and others 88 have collected hundreds of proposed biomarker candidates. However, even though a large number of biomarker candidates have been proposed 99 , very few have progressed to the stage of clinical validation along their clinical trial pathway.…”

Section: Biomarker Research Growthmentioning

confidence: 99%

Biomarkers for early cancer detection: A landscape view of recent advancements, spotlighting pancreatic and liver cancers

Tenchov,

Sapra,

Sasso

et al. 2024

Preprint

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Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcome thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are amongst the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant development in the field of early cancer detection biomarkers. Here we provide an overview of recent advancement in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientiﬁc information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts, cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, known as remarkably difficult to detect early, with particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers, to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of the article stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

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A multi-task positive-unlabeled learning framework to predict secreted proteins in human body fluids

He,

Wang,

Xie

et al. 2023

Complex Intell. Syst.

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Body fluid biomarkers are very important, because they can be detected in a non-invasive or minimally invasive way. The discovery of secreted proteins in human body fluids is an essential step toward proteomic biomarker identification for human diseases. Recently, many computational methods have been proposed to predict secreted proteins and achieved some success. However, most of them are based on a manual negative dataset, which is usually biased and therefore limits the prediction performances. In this paper, we first propose a novel positive-unlabeled learning framework to predict secreted proteins in a single body fluid. The secreted protein discovery in a single body fluid is transformed into multiple binary classifications and solved via multi-task learning. Also, an effective convolutional neural network is employed to reduce the overfitting problem. After that, we then improve this framework to predict secreted proteins in multiple body fluids simultaneously. The improved framework adopts a globally shared network to further improve the prediction performances of all body fluids. The improved framework was trained and evaluated on datasets of 17 body fluids, and the average benchmarks of 17 body fluids achieved an accuracy of 89.48%, F1 score of 56.17%, and PRAUC of 58.93%. The comparative results demonstrate that the improved framework performs much better than other state-of-the-art methods in secreted protein discovery.

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LiqBioer: a manually curated database of cancer biomarkers in body fluid

Cited by 4 publications

References 23 publications

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Biomarkers for early cancer detection: A landscape view of recent advancements, spotlighting pancreatic and liver cancers

A multi-task positive-unlabeled learning framework to predict secreted proteins in human body fluids

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