Evidence map›Paper›PMID 39712862›Full record

ArticleIEEE data descriptions2024

Descriptor:

Arif Harmanci, Luyao Chen, Miran Kim, Xiaoqian Jiang

Abstract read
In one paragraph

Article in IEEE data descriptions, 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
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

4 authors.

Arif HarmanciDepartment of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030 USA.ORCID 0000-0002-9696-1118
Luyao ChenDepartment of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030 USA.ORCID 0000-0003-4698-5784
Miran KimDepartment of Mathematics, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0000-0003-3564-6090
Xiaoqian JiangDepartment of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030 USA.ORCID 0000-0001-9933-2205

Funding

Secure Outsourcing of Genotype Imputation for Privacy-aware Genomic Analysis (RO1HE21)R01HG012604 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Arif Ozgun Harmanci · 2023 to 2026
$2.2M
iDASH Genome Privacy and Security Competition WorkshopR13HG012902 · NHGRI · YALE UNIVERSITY · PI Xiaoqian Jiang, Tsung-Ting Kuo · 2023 to 2026
$80k
NHGRI NIH HHS R01 HG012604NHGRI NIH HHS R13 HG012902
6 · The paper itself

Abstract

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

Indexed as

Genomic privacyhomomorphic encryption (HE)transformer model

Identifiers

PMID39712862
PMCPMC11660429

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.