Evidence map›Paper›PMID 38370127›Full record

ArticlePatterns (New York, N.Y.)2024

shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.

Li-Ju Wang, Michael Ning, Tapsya Nayak, Michael J Kasper, Satdarshan P Monga, Yufei Huang, Yidong Chen, Yu-Chiao Chiu

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Li-Ju WangCancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.
Michael NingCancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.
Tapsya NayakGreehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Michael J KasperCancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.
Satdarshan P MongaDepartment of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
Yufei HuangDepartment of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
Yidong ChenGreehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Yu-Chiao ChiuCancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHRISTOPHER J. BAKKENIST · 1988 to 2026
$158.0M
TISSUE CULTURE---COREP30CA054174 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Lei Zheng · 1991 to 2026
$59.1M
INSTITUTE FOR INTEGRATION OF MEDICINE & SCIENCE: A PARTNERSHIP TO IMPROVE HEALTHUL1RR025767 · NCRR · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI CLARK, ROBERT A · 2008 to 2011
$23.8M
Institute for Integration of Medicine & Science: A Partnership to Improve HealthUM1TR004538 · NCATS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI ROBERT A CLARK, Kenneth M Hargreaves · 2023 to 2026
$22.3M
Pittsburgh Liver Research CenterP30DK120531 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Shuchang Silvia Liu · 2019 to 2026
$10.9M
Targeting tumor metabolism and immune environment via beta-catenin: Towards precision medicine in HCCR01CA251155 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LUJAMBIO, AMAIA, MONGA, SATDARSHAN SINGH · 2020 to 2024
$3.1M
Investigating Multifactorial Beta-catenin Activation in Hepatocellular CancersR01CA250227 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Xin Chen, Satdarshan Singh Monga · 2021 to 2026
$3.0M
m6A-suite: an informatics pipeline and resource for elucidating roles of m6A epitranscriptome in cancerU01CA279618 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HUANG, YUFEI · 2023 to 2025
$1.8M
Enhancing AI-readiness of multi-omics data for cancer pharmacogenomicsR00CA248944 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHIU, YU-CHIAO · 2022 to 2024
$1.1M
High-Throughput Computing for Genomics and Bioinformatics ResearchS10OD028483 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEE, ADRIAN V · 2021 to 2021
$574k
In silico screening for immune surveillance adaptation in cancer using Common Fund data resourcesR03OD036494 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHIU, YU-CHIAO, GONG, YI-NAN · 2023 to 2023
$318k
NCATS NIH HHS UM1 TR004538NCI NIH HHS P30 CA047904NCI NIH HHS P30 CA054174NCI NIH HHS R00 CA248944NCI NIH HHS R01 CA250227NCI NIH HHS R01 CA251155NCI NIH HHS U01 CA279618NCRR NIH HHS UL1 RR025767NIDDK NIH HHS P30 DK120531NIH HHS R03 OD036494NIH HHS S10 OD028483
6 · The paper itself

Abstract

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for

Indexed as

cancerdeep learningdrug responsepredictionR Shiny appweb tool

Identifiers

PMID38370127
PMCPMC10873157

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.