Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors at 2 institutions in 1 country.
Tamara RaschkaDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.ORCID 0000-0003-2332-6137
Meemansa SoodDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.ORCID 0000-0002-0862-3806
Bruce SchultzDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.ORCID 0000-0003-4492-9463
Aybuge AltayDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.ORCID 0000-0001-7341-7091
Christian EbelingDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
Holger FröhlichDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
University of Bonn · DEFraunhofer Institute for Algorithms and Scientific Computing · DE
Funding
Vascular Structure and Function in Cognitive AgingP01AG003949 · NIA · YESHIVA UNIVERSITY · PI Richard B. LIPTON · 1985 to 2026
$73.9M
SUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
EPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3M
THE PGRN/TDP-43 AXIS IN ALZHEIMER?S DISEASE AND NEURODEGENERATIONP50AG016574 · NIA · MAYO CLINIC ROCHESTER · PI PETERSEN, RONALD C · 1999 to 2018
$36.9M
Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI REIMAN, ERIC MICHAEL · 2001 to 2020
$32.5M
Integrative 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
$26.0M
Rush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7M
Integrating 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
$24.6M
Tox-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
$24.6M
RISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4M
Multi-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer's Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.
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 Socratic graph.
AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. · full record | Socratic