ArticleBriefings in bioinformatics2019
A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.
Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models.Environmental health perspectives · 2024Pooled it
- Systematic comparison of ranking aggregation methods for gene lists in experimental results.Bioinformatics (Oxford, England) · 2022Pooled it
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- Article
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- BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.Nature communications · 2026Article
- Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer.bioRxiv : the preprint server for biology · 2026Article
- Functional genomics for improving adoptive T-cell transfer therapies.Journal for immunotherapy of cancer · 2025Review
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Rank Aggregation Methods and Tools in Genomic Data Analysis.Current genomics · 2025Review
- Edge and modular significance assessment in individual-specific networks.Scientific reports · 2023Article
- Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.Genome research · 2023Article
- Transcriptomic pan-cancer analysis using rank-based Bayesian inference.Molecular oncology · 2023Article
- A Comparative Study of Rank Aggregation Methods in Recommendation Systems.Entropy (Basel, Switzerland) · 2023Article
- Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease.Brain sciences · 2020Article
- Inferring a consensus problem list using penalized multistage models for ordered data.The annals of applied statistics · 2020Article
- Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction.Brain sciences · 2020Article
- Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019.Frontiers in psychiatry · 2020Article
- Core transcriptional signatures of phase change in the migratory locust.Protein & cell · 2019Article
- A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.Statistics in medicine · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.