Evidence map›Paper›PMID 38343707›Full record

ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2023

R-Mixup: Riemannian Mixup for Biological Networks.

Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, Ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2023. 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

9 authors.

Xuan KanDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Zimu LiPritzker School of Molecular, Engineering, University of Chicago, Chicago, IL, USA.
Hejie CuiDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Yue YuSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Ran XuDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Shaojun YuDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Zilong ZhangSchool of Statistics, University of International Business and Economics, Beijing, China.
Ying GuoDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Carl YangDepartment of Computer Science, Emory University, Atlanta, GA, USA.

Funding

Statistical ICA Methods for Analysis & Integration of Multi-dimensional Data Diversity SupplementR01MH105561 · NIMH · EMORY UNIVERSITY · PI GUO, YING, KANG, JIAN · 2014 to 2024
$4.3M
Statistical Methods for Analyzing Complex, Multi-dimensional Data from Cross-sectional and Longitudinal Mental Health StudiesR01MH118771 · NIMH · EMORY UNIVERSITY · PI GUO, YING, MANATUNGA, AMITA K. · 2019 to 2023
$3.1M
Method Development of Agreement Measures and Applications in Mental HealthR01MH079448 · NIMH · EMORY UNIVERSITY · PI GUO, YING, MANATUNGA, AMITA K. · 2008 to 2016
$2.2M
Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected DataK25DK135913 · NIDDK · EMORY UNIVERSITY · PI Ji Carl Yang · 2023 to 2026
$687k
NIDDK NIH HHS K25 DK135913NIMH NIH HHS R01 MH079448NIMH NIH HHS R01 MH105561NIMH NIH HHS R01 MH118771
6 · The paper itself

Abstract

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-Mixup, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-Mixup leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the

Indexed as

biological networkdata augmentationgeometric deep learning

Identifiers

PMID38343707
PMCPMC10853987

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.