ArticlePLoS computational biology2017
Complete hazard ranking to analyze right-censored data: An ALS survival study.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
The trial behind it
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
18 citing papers in PubMed.
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Encoding functional edges in graphs to model spatially varying relationships in the tumor microenvironment.NPJ artificial intelligence · 2026Article
- Individualized dynamic risk assessment and treatment selection for multiple myeloma.British journal of cancer · 2025Article
- Review
- Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.Life (Basel, Switzerland) · 2023Article
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Outcome and Biomarker Supervised Deep Learning for Survival Prediction in Two Multicenter Breast Cancer Series.Journal of pathology informatics · 2022Article
- A proportional-hazards model for survival analysis and long-term survivors modeling: application to amyotrophic lateral sclerosis data.Journal of applied statistics · 2022Article
- Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis.Molecular neurodegeneration · 2021Article
- Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.NPJ precision oncology · 2021Article
- Manifold learning for amyotrophic lateral sclerosis functional loss assessment : Development and validation of a prognosis model.Journal of neurology · 2021Article
- Prognostic model for multiple myeloma progression integrating gene expression and clinical features.GigaScience · 2019Article
- ALS/SURV: a modification of the CAFS statistic.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2019Article
- Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.Neuroinformatics · 2019Article
- Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.Scientific reports · 2019Article
- Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions.Frontiers in neuroscience · 2019Review
- Individualized predictions for clinical milestone in amyotrophic lateral sclerosis: A multialgorithmic approach.Digital healthArticle
- Smart mating: the cognitive ability of females influences their preference for male cognitive ability.Behavioral ecology : official journal of the International Society for Behavioral EcologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.
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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.