Evidence map›Paper›PMID 41783808›Full record

ArticleNPJ artificial intelligence2026

Encoding functional edges in graphs to model spatially varying relationships in the tumor microenvironment.

Ashley P Tsang, Santhoshi N Krishnan, Reva Kulkarni, Sagnik Bhadury, Marina Pasca di Magliano, Timothy L Frankel, Arvind Rao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ artificial intelligence, 2026. 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
–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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

7 authors.

Ashley P Tsang *Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.
Santhoshi N Krishnan *Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.
Reva KulkarniGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.
Sagnik BhaduryGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.
Marina Pasca di MaglianoDepartment of Surgery, University of Michigan, Ann Arbor, MI USA.
Timothy L FrankelDepartment of Surgery, University of Michigan, Ann Arbor, MI USA.
Arvind RaoGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI USA.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
Synthesizing Image-derived Heterogeneity with Genomic measurements for Assessing Disease Aggressiveness in Lower Grade GliomasR37CA214955 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KURTEK, SEBASTIAN, RAO, ARVIND · 2018 to 2024
$3.9M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Epithelial-immune cell crosstalk during injury and recovery in acute pancreatitisR01DK128102 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Timothy Louis Frankel · 2022 to 2026
$2.2M
Leveraging artificial intelligence/machine learning-based technology to overcome specialized training and technology barriers for the diagnosis and prognostication of colorectal cancer in AfricaU01CA287852 · NCI · AGA KHAN UNIVERSITY (KENYA) · PI BALIS, ULYSSES GREGORY JOHN, RAO, ARVIND · 2023 to 2025
$750k
BLRD VA I01 BX005777NCI NIH HHS P30 CA046592NCI NIH HHS R37 CA214955NCI NIH HHS U01 CA287852NIDDK NIH HHS R01 DK128102NIGMS NIH HHS T32 GM141746
6 · The paper itself

Abstract

Comprehensive characterization of the tumor microenvironment (TME) is essential for understanding cancer progression and developing effective, patient-specific therapies. Spatial context of the TME is particularly important, and exists across multiple scales-from the molecular to cellular to tissue levels. However, current methods are modality-specific and lack flexibility in effectively modeling the TME. We introduce SPIFEE, a flexible graph deep learning framework designed to model the TME and uncover spatial insights across multiple levels of biological organization. SPIFEE increases the expressivity of graph-based representations by directly encoding spatially varying functional vectors into graph edges. Additionally, it represents graph nodes as unique TME entities (

Indexed as

CancerComputational biology and bioinformaticsSystems biology

Identifiers

PMID41783808
PMCPMC12953152

What OpenQuestion holds

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Registered trials

None linked

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.