Evidence map›Paper›PMID 37025902›Full record

ArticleHeliyon2023

Convolutional neural network for biomarker discovery for triple negative breast cancer with RNA sequencing data.

Xiangning Chen, Justin M Balko, Fei Ling, Yabin Jin, Anneliese Gonzalez, Zhongming Zhao, Jingchun Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 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. Review
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

7 authors.

Xiangning Chen410 AI, LLC, 10 Plummer Ct, Germantown, MD, 20876, USA.
Justin M BalkoDepartment of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2101 W End Ave, Nashville, TN, 37240, USA.
Fei LingSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Yabin JinClinical Research Institute, The First People's Hospital of Foshan, Foshan, China.
Anneliese GonzalezDepartment of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, TX77030, USA.
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Jingchun ChenNevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA.

Funding

TISSUE CoreP50CA098131 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PARK, BEN HO, PIETENPOL, JENNIFER A · 2003 to 2024
$48.7M
NCI NIH HHS P50 CA098131
6 · The paper itself

Abstract

Triple negative breast cancers (TNBCs) are tumors with a poor treatment response and prognosis. In this study, we propose a new approach, candidate extraction from convolutional neural network (CNN) elements (CECE), for discovery of biomarkers for TNBCs. We used the GSE96058 and GSE81538 datasets to build a CNN model to classify TNBCs and non-TNBCs and used the model to make TNBC predictions for two additional datasets, the cancer genome atlas (TCGA) breast cancer RNA sequencing data and the data from Fudan University Shanghai Cancer Center (FUSCC). Using correctly predicted TNBCs from the GSE96058 and TCGA datasets, we calculated saliency maps for these subjects and extracted the genes that the CNN model used to separate TNBCs from non-TNBCs. Among the TNBC signature patterns that the CNN models learned from the training data, we found a set of 21 genes that can classify TNBCs into two major classes, or CECE subtypes, with distinct overall survival rates (

Indexed as

Biomarker discoveryConvolutional neural networkMachine learningRNA sequencingTriple negative breast cancer

Identifiers

PMID37025902
PMCPMC10070674

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.