ArticlebioRxiv : the preprint server for biology2024
Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data.
Akihiro Kitani, Yusuke Matsui
Abstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from itWhat 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 registryThe 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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3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
2 authors.
Akihiro KitaniBiomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.ORCID 0009-0004-2220-8109 Yusuke MatsuiBiomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.ORCID 0000-0003-3977-4313 Funding
Vascular Structure and Function in Cognitive AgingP01AG003949 · NIA · YESHIVA UNIVERSITY · PI Richard B. LIPTON · 1985 to 2026
$73.9MSUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6MSUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1MEPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3MTHE PGRN/TDP-43 AXIS IN ALZHEIMER?S DISEASE AND NEURODEGENERATIONP50AG016574 · NIA · MAYO CLINIC ROCHESTER · PI PETERSEN, RONALD C · 1999 to 2018
$36.9MResearch Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI REIMAN, ERIC MICHAEL · 2001 to 2020
$32.5MIntegrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WANG, MINGHUI, ZHANG, BIN · 2013 to 2022
$26.0MResearch Education ComponentP30AG072980 · NIA · BANNER HEALTH · PI ALIREZA ATRI · 2021 to 2026
$24.9MRush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7MIntegrating the exposome and methylome to inform brain molecular changes in ADRD across established diverse cohorts.U01AG046139 · NIA · UNIVERSITY OF FLORIDA · PI ERTEKIN-TANER, NILUFER, FUNK, CORY · 2013 to 2022
$24.6MTox-AD: a new multi-institute tri-consortium data resource in the AD Knowledge PortalU24AG061340 · NIA · SAGE BIONETWORKS · PI Laura Michelle Heath, Susheel Varma · 2018 to 2026
$24.6MRISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4MNIA NIH HHS P01 AG003949NIA NIH HHS P01 AG017216NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072980NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG025711NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG018023NIA NIH HHS R01 AG032990NIA NIH HHS R01 AG036836NIA NIH HHS RF1 AG057440NIA NIH HHS U01 AG006786NIA NIH HHS U01 AG046139NIA NIH HHS U01 AG046152NIA NIH HHS U01 AG046170NIA NIH HHS U01 AG061356NIA NIH HHS U24 AG061340NINDS NIH HHS R01 NS080820NINDS NIH HHS U24 NS072026
6 · The paper itselfAbstract
Alzheimer's disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available at https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.
Indexed as
Alzheimer’s diseasemachine learningresilience gene analyzerShapley additive explanationstranscriptomics
Identifiers
PMID39253457
PMCPMC11383294
What Socratic holds
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LicenceCC BY
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