ArticleBMC bioinformatics2022
Evaluation of statistical approaches for association testing in noisy drug screening data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.Aesthetic surgery journal · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Reassessing pharmacogenomic cell sensitivity with multilevel statistical models.Biostatistics (Oxford, England) · 2023Article
- Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.ACS pharmacology & translational science · 2023Article
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.Nucleic acids research · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment.
resultsTo address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets.
conclusionsWe observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.
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Registered trials
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