Efficient and scalable construction of clinical variable networks for complex diseases with RAMEN.

Yiwei Xiong, Jingtao Wang, Xiaoxiao Shang, Tingting Chen, Douglas D Fraser, Gregory J Fonseca, Simon Rousseau, Jun Ding

Abstract read

In one paragraph

Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yiwei XiongMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.

Jingtao WangMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.

Xiaoxiao ShangMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, QC H3A 0B9, Canada.

Tingting ChenMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Hematology Department, Beijing Luhe Hospital, Capital Medical University, Xinhua South Road No. 82, Tongzhou District, Beijing 101149, China.

Douglas D FraserChildren's Health Research Institute, Victoria Research Laboratories, 800 Commissioners Road East, London, ON N6C 2V5, Canada; Lawson Health Research Institute, London, ON N6C 2R5, Canada; Department of Pediatrics, Western University, London, ON N6A 5C1, Canada; Department of Physiology & Pharmacology, Western University, London, ON N6A 5C1, Canada; Department of Clinical Neurological Sciences, Western University, London, ON N6A 5C1, Canada.

Gregory J FonsecaMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada. Electronic address: gregory.fonseca@mcgill.ca.

Simon RousseauMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada. Electronic address: simon.rousseau@mcgill.ca.

Jun DingMeakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; School of Computer Science, McGill University, 3480 Rue University, Montreal, QC H3A 2A7, Canada; Mila-Quebec AI Institute, 6666 Rue Saint-Urbain, Montreal, QC H2S 3H1, Canada. Electronic address: jun.ding@mcgill.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the interplay among clinical variables-such as demographics, symptoms, and laboratory results-and their relationships with disease outcomes is critical for advancing diagnostics and understanding mechanisms in complex diseases. Existing methods fail to capture indirect or directional relationships, while existing Bayesian network learning methods are computationally expensive and only infer general associations without focusing on disease outcomes. Here we introduce random walk- and genetic algorithm-based network inference (RAMEN), a method for Bayesian network inference that uses absorbing random walks to prioritize outcome-relevant variables and a genetic algorithm for efficient network refinement. Applied to COVID-19 (Biobanque québécoise de la COVID-19), intensive care unit (ICU) septicemia (MIMIC-III), and COPD (CanCOLD) datasets, RAMEN reconstructs networks linking clinical markers to disease outcomes, such as elevated lactate levels in ICU patients. RAMEN demonstrates advantages in computational efficiency and scalability compared to existing methods. By modeling outcome-specific relationships, RAMEN provides a robust tool for uncovering critical disease mechanisms, advancing diagnostics, and enabling personalized treatment strategies.

Indexed as

AlgorithmsCOVID-19Pulmonary Disease, Chronic ObstructiveSepsisBayes TheoremHumansIntensive Care UnitsSARS-CoV-2absorbing random walkBayesian network inferencechronic obstructive pulmonary diseaseclinical variable networkscomplex diseasesCOVID-19CP: Systems biologygenetic algorithmmulti-omicspersonalized medicinesepticemia

Identifiers

PMID40215965

DOI10.1016/j.crmeth.2025.101022

PMCPMC12256955

What OpenQuestion holds

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LicenceCC BY-NC

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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.