DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types

Improving the classification of neuropsychiatric conditions using gene ontology terms as features

(2019) Thomas P. Quinn et al.   AMERICAN JOURNAL OF MEDICAL GENETICS PART B-NEUROPSYCHIATRIC GENETICS

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

(2016) Lujia Chen et al.   BMC BIOINFORMATICS

Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy

(2016) Weiguo Zhang et al.   Nature Communications

DNA-damage related genes and clinical outcome in hormone receptor positive breast cancer

(2016) Cristina Nieto-Jiménez et al.   Oncotarget

ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

(2016) Jie Tan et al.   mSystems

Classification of lung cancer using ensemble-based feature selection and machine learning methods

(2015) Zhihua Cai et al.   Molecular BioSystems

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

(2015) Antonio Colaprico et al.   NUCLEIC ACIDS RESEARCH

Robustness of Random Forest-based gene selection methods

(2014) Miron Bartosz Kursa   BMC BIOINFORMATICS

New Strategies for Triple-Negative Breast Cancer--Deciphering the Heterogeneity

(2014) I. A. Mayer et al.   CLINICAL CANCER RESEARCH

The Cancer Genome Atlas Pan-Cancer analysis project

(2013) John N Weinstein et al.   NATURE GENETICS

Differential expression analysis for sequence count data

(2010) Simon Anders et al.   GENOME BIOLOGY

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

(2009) Joel S. Parker et al.   JOURNAL OF CLINICAL ONCOLOGY

A comparative study of different machine learning methods on microarray gene expression data

(2008) Mehdi Pirooznia et al.   BMC GENOMICS