ArticleAdvances in neural information processing systems2024
Fairness-Aware Estimation of Graphical Models.
Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen
Abstract read
In one paragraphArticle in Advances in neural information processing systems, 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
–field-weighted citation impact
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 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.
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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
5 authors.
Zhuoping ZhouUniversity of Pennsylvania.
Davoud Ataee TarzanaghUniversity of Pennsylvania.
Bojian HouUniversity of Pennsylvania.
Qi LongUniversity of Pennsylvania.
Li ShenUniversity of Pennsylvania.
Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0MPeripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo · 2023 to 2026
$42.0MTechnology Identification and Training CoreP30AG073105 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DEMIRIS, GEORGE, KARLAWISH, JASON H · 2021 to 2025
$21.2MUltrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7MArtificial Intelligence Strategies for Alzheimer's Disease ResearchU01AG066833 · NIA · CEDARS-SINAI MEDICAL CENTER · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2022 to 2025
$6.7MTranslational big data analytic approaches to advance drug repurposing for Alzheimer's diseaseR01AG071470 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON, NING, XIA · 2021 to 2025
$3.8MAdvancing Analysis of Multi-omics Data in Alzheimer's Disease ResearchRF1AG063481 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LONG, QI · 2019 to 2020
$3.8MStatistical Modeling of Alzheimer's Disease Progression Integrating Brain Imaging and -Omics DataR01AG071174 · NIA · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KUNDU, SUPRATEEK, LONG, QI · 2021 to 2025
$3.3MNovel integrative imaging genetics analysis for Alzheimer's disease riskand progressionRF1AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2021 to 2021
$1.9MInformatics Algorithms for Genomic Analysis of Brain Imaging DataR01LM013463 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., SAYKIN, ANDREW J · 2020 to 2023
$1.4MRobust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparitiesU01CA274576 · NCI · UNIVERSITY OF PENNSYLVANIA · PI JIANG, XIAOQIAN, LONG, QI · 2023 to 2025
$1.2MNCI NIH HHS U01 CA274576NIA NIH HHS P30 AG073105NIA NIH HHS R01 AG071174NIA NIH HHS R01 AG071470NIA NIH HHS RF1 AG063481NIA NIH HHS RF1 AG068191NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG066833NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG074879NLM NIH HHS R01 LM013463
6 · The paper itselfAbstract
This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the
Identifiers
PMID40855882
PMCPMC12373123
What Socratic holds
Textmetadata
LicenceTDM
Read underepoch 390