AIMC Topic: Genes, Neoplasm
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Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.
Nature biomedical engineering
Jan 9, 2025
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the dev...
Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning.
Cells
Jan 15, 2022
Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discover...
Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer.
Scientific reports
Aug 13, 2021
The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-s...
An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 6, 2021
Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model)...
Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.
Aging
Mar 3, 2021
We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained ...
GVES: machine learning model for identification of prognostic genes with a small dataset.
Scientific reports
Jan 11, 2021
Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction a...
Application of BERT to Enable Gene Classification Based on Clinical Evidence.
BioMed research international
Oct 7, 2020
The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of gene...
Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification.
IEEE journal of biomedical and health informatics
Feb 20, 2020
Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold...
A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.
Blood cancer journal
Oct 1, 2019
A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.
BMC cancer
Sep 5, 2019
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
Genes, Neoplasm
Adenocarcinoma of Lung
Disease-Free Survival
Proportional Hazards Models
Multivariate Analysis
Male
Kaplan-Meier Estimate
Female
Middle Aged
Aged
Disease Progression
Survival Rate
Lung Neoplasms
Transcriptome
Humans
Biomarkers, Tumor
Gene Expression Regulation, Neoplastic
Machine Learning
Follow-Up Studies
Prognosis
Databases, Genetic
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