Evidence map›Paper›PMID 37711665›Full record

ArticleThe annals of applied statistics2023

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER.

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li, Veerabhadran Baladandayuthapani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The annals of applied statistics, 2023. 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

5 authors.

Tsung-Hung YaoDepartment of Biostatistics, University of Michigan at Ann Arbor.
Zhenke WuDepartment of Biostatistics, University of Michigan at Ann Arbor.
Karthik BharathSchool of Mathematical Sciences, University of Nottingham.
Jinju LiDepartment of Biostatistics, University of Michigan at Ann Arbor.
Veerabhadran BaladandayuthapaniDepartment of Biostatistics, University of Michigan at Ann Arbor.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 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
Bayesian Network-Based Integrative Genomics Methods for Precision MedicineR01CA244845 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BALADANDAYUTHAPANI, VEERABHADRAN, MORRIS, JEFFREY S · 2021 to 2024
$1.8M
NCI NIH HHS P30 CA046592NCI NIH HHS R01 CA244845NCI NIH HHS R37 CA214955
6 · The paper itself

Abstract

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (R

Indexed as

Approximate Bayesian ComputationDirichlet Diffusion TreesPatient Derived XenograftPrecision MedicineTree-Based Clustering

Identifiers

PMID37711665
PMCPMC10501503

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.