Evidence map›Paper›PMID 42807651›Full record

ArticleThe annals of applied statistics2026

BAYESIAN EMPIRICAL LIKELIHOOD FOR ULTRA-HIGH DIMENSIONAL VARIABLE SELECTION WITH APPLICATION TO DRUG SENSITIVITY.

Can Xu, Zeyu Lu, Yichen Cheng, Qifan Song, Yichuan Zhao, Xinlei Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The annals of applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Can XuSouthern Methodist University.
Zeyu LuUniversity of Texas at Arlington.ORCID 0000-0002-4272-8388
Yichen ChengGeorgia State University.
Qifan SongPurdue University.
Yichuan ZhaoGeorgia State University.
Xinlei WangDivision of Data Science, University of Texas at Arlington.ORCID 0000-0002-8561-6511

Funding

Statistical and Deep Generative Modeling for Enhanced CyTOF Data Interpretation and DiscoveryR01GM160515 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI WANG, XINLEI · 2025 to 2025
$1.3M
Towards Rational Discovery of Combination Therapies: LLM-Enhanced Gene Function Mapping to Explainable Drug Synergy PredictionR01LM015497 · NLM · UNIVERSITY OF TEXAS ARLINGTON · PI Junzhou Huang, Xinlei Wang · 2026 to 2026
$632k
Normalizing Gene Expression Data from Formalin-Fixed Paraffin-Embedded SamplesR15GM131390 · NIGMS · SOUTHERN METHODIST UNIVERSITY · PI WANG, XINLEI · 2019 to 2019
$432k
NIGMS NIH HHS R01 GM160515NIGMS NIH HHS R15 GM131390NLM NIH HHS R01 LM015497
6 · The paper itself

Abstract

Predicting drug sensitivity and selecting informative biomarkers are fundamental challenges in precision oncology. These tasks are complicated by the ultra-high dimensional nature of omics data, where the number of covariates

Indexed as

Active setDouble penaltyEstimating equationLagrange multiplierPath-wise coordinate optimizationSparse learning

Identifiers

PMID42807651
PMCPMC13618762

What OpenQuestion holds

Textmetadata
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.