Mary L. Stackpole

Methylation plus alpha-fetoprotein blood test for early detection of HCC in at-risk populations

Neehar D. Parikh, Chun-Chi Liu, Matthew DeMaio, Angela H. Yeh et autres

BACKGROUND: Semi-annual abdominal ultrasound plus alpha-fetoprotein (AFP) is recommended for HCC surveillance but has limited performance, particularly in patients with metabolic dysfunction-associated steatotic liver disease. We aimed to validate a blood-based test combining cell-free DNA methylation profiling with AFP to improve HCC …

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Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test

Weihua Zeng, Weihua Zeng, S. Guo-Dong Li, Shuo Li et autres

Plasma cell-free DNA (cfDNA), originating from multiple organs, holds significant potential for noninvasive diagnostics and prognostics. Current cfDNA methylation assays primarily focus on single clinical indications by targeting specific genomic loci. In contrast, comprehensive profiling of cfDNA methylome can enable simultaneous detection …

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Correction: cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution

Shuo Li, Weihua Zeng, Xiaohui Ni, Y. Zhou et autres

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Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

BACKGROUND: Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. …

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Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Abstract Background Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic …

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Additional file 1 of Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Additional file 1: Tables S1–S7. A compiled multi-tab Excel file containing all supplementary tables referenced in the manuscript, including clinical and demographic characteristics of all datasets, as well as AUROC values and corresponding confidence intervals for all methods across all datasets.

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Additional file 2 of Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Additional file 2: Figs. S1–S19. A single file containing all supplementary figures referenced in the manuscript. Captions and legends for each supplementary figure are included within the file.

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Additional file 2 of Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Additional file 2: Figs. S1–S19. A single file containing all supplementary figures referenced in the manuscript. Captions and legends for each supplementary figure are included within the file.

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Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Abstract Background Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic …

us (code pays fourni par la source)

Additional file 1 of Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu et autres

Additional file 1: Tables S1–S7. A compiled multi-tab Excel file containing all supplementary tables referenced in the manuscript, including clinical and demographic characteristics of all datasets, as well as AUROC values and corresponding confidence intervals for all methods across all datasets.

us (code pays fourni par la source)

Author Correction: Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

S. Guo-Dong Li, Zorawar S. Noor, Weihua Zeng, Mary L. Stackpole et autres

In the version of the article initially published, the Competing interests section was incomplete and has now been expanded in the HTML and PDF versions of the article to: “X.J.Z., W.L., and W.H.W. are co-founders of EarlyDiagnostics. X.J.Z and W.H.W serve on …

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Noninvasive prognostication of hepatocellular carcinoma based on cell-free DNA methylation

Ran Hu, Benjamin V. Tran, Shuo Li, Mary L. Stackpole et autres

BACKGROUND: The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet …

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