Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing 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
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.
Yang Yu *Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.ORCID http://orcid.org/0009-0006-1475-7487
Shuang Wang *Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA.
Meichen YuIndiana Alzheimer's Disease Research Center, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0003-4551-0269
Kyle McCrocklinDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA.
Jing-Qiong KangDepartment of Neurology & Pharmacology, Vanderbilt University Medical Center, Vanderbilt Kennedy Center of Human Development, Vanderbilt University, Nashville, TN, USA.ORCID http://orcid.org/0000-0003-1079-5436
Qin MaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA. qin.ma@osumc.edu.ORCID http://orcid.org/0000-0002-3264-8392
Dong XuDepartment of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA. xudong@missouri.edu.ORCID http://orcid.org/0000-0002-4809-0514
Juexin WangDepartment of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA. wangjuex@iu.edu.ORCID http://orcid.org/0000-0002-2260-4310
Funding
Tumor Microenvironment and Metastasis ProgramP30CA082709 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI David W Clapp · 1999 to 2026
$59.3M
Peripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo · 2023 to 2026
$42.0M
Research Education ComponentP30AG072976 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI ANDREW J SAYKIN · 2021 to 2026
$24.1M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
TriState SenNET (Lung and Heart) Tissue Map and Atlas consortiumU54AG075931 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TOREN FINKEL, Melanie Koenigshoff · 2021 to 2026
$14.0M
KBASE2: Korean Brain Aging Study, Longitudinal Endophenotypes and Systems BiologyU01AG072177 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI LEE, DONG YOUNG, NHO, KWANGSIK TIMOTHY · 2021 to 2025
$11.2M
The Role of the Y Chromosome in Bladder Tumor Development, Growth And ProgressionP01CA278732 · NCI · CEDARS-SINAI MEDICAL CENTER · PI SUNGYONG YOU · 2023 to 2026
$10.7M
NHP CoreP01AI177687 · NIAID · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Dan H. Barouch · 2023 to 2026
$7.4M
Memory Circuitry in MCI and Early Alzheimer’s Disease Prodrome: Molecular DriversR01AG019771 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 2001 to 2021
$6.9M
Multi-view self-supervised deep learning for biological sequences and beyondR35GM126985 · NIGMS · UNIVERSITY OF SOUTH FLORIDA · PI DONG XU · 2018 to 2026
$3.8M
Promoting Protein Trafficking with 4-phenylbutyrate to Treat Genetic EpilepsyR01NS121718 · NINDS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jing-Qiong Kang · 2022 to 2026
$2.2M
Indiana Genomics Research Training Program for Data Scientists (INGEN4DS)R25HG012325 · NHGRI · INDIANA UNIVERSITY INDIANAPOLIS · PI Kun Huang, Yunlong Liu · 2022 to 2026
$1.3M
NCI NIH HHS P01 CA278732NCI NIH HHS P30 CA082709NHGRI NIH HHS R25 HG012325NIAID NIH HHS P01 AI177687NIA NIH HHS P30 AG072976NIA NIH HHS R01 AG019771NIA NIH HHS U01 AG068057NIA NIH HHS U01 AG072177NIA NIH HHS U19 AG074879NIA NIH HHS U54 AG075931NIDDK NIH HHS R01 DK138504NIDDK NIH HHS R21 DK140693NIGMS NIH HHS R01 GM152585NIGMS NIH HHS R35 GM126985NINDS NIH HHS R01 NS121718U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01DK138504
6 · The paper itself
Abstract
The spatial organization of cells plays a pivotal role in shaping tissue functions and phenotypes in various biological systems and diseased microenvironments. However, the topological principles governing interactions among cell types within spatial patterns remain poorly understood. Here, we present the triangulation cellular community motif neural network (TrimNN), a graph-based deep learning framework designed to identify conserved spatial cell organization patterns, termed cellular community (CC) motifs, from spatial transcriptomics and proteomics data. TrimNN employs a semi-divide-and-conquer approach to efficiently detect overrepresented topological motifs of varying sizes in a triangulated space. By uncovering CC motifs, TrimNN reveals key associations between spatially distributed cell-type patterns and diverse phenotypes. These insights provide a foundation for understanding biological and disease mechanisms and offer potential biomarkers for diagnosis and therapeutic interventions.
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.
TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues. · full record | OpenQuestion