ArticleBMC bioinformatics2020
Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 49 citations in OpenAlex.
- RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer.International journal of cancer · 2026Article
- A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.Scientific reports · 2025Article
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).World journal of surgical oncology · 2024Article
- Review
- SuperPATH-Current Status of Evidence and Further Investigations: A Scoping Review and Quality Assessment.Journal of clinical medicine · 2023Article
- Article
- Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer.Frontiers in oncology · 2022Article
- Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging.Abdominal radiology (New York) · 2021Article
- Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis.Gland surgery · 2021Article
- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
- Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFinding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples.
resultsA data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral). The aim of this work is to understand the genomic activity in each class and find relevant genes as indicators for each class with nearly 99% accuracy. The system identified groups of differentially expressed genes (RTN1, HLA-DMB, MRI1) that are able to differentiate samples among the three classes.
conclusionThe proposed method was able to detect sets of genes that can identify different laterality classes. The resulting genes are found to be strongly correlated with disease progression. HLA-DMB and EIF4G2, which are detected in the set of genes can detect the left laterality, were reported earlier to be in the same pathway called Allograft rejection SuperPath.
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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 Socratic graph.