Related references
Note: Only part of the references are listed.Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis
Andrew J. Sedgewick et al.
BIOINFORMATICS (2019)
Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models
Vineet K. Raghu et al.
THORAX (2019)
PARP1 rs1805407 Increases Sensitivity to PARP1 Inhibitors in Cancer Cells Suggesting an Improved Therapeutic Strategy
Irina Abecassis et al.
SCIENTIFIC REPORTS (2019)
piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks
Dimitris V. Manatakis et al.
BIOINFORMATICS (2018)
Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients
Georgios D. Kitsios et al.
FRONTIERS IN MICROBIOLOGY (2018)
Comparison of strategies for scalable causal discovery of latent variable models from mixed data
Vineet K. Raghu et al.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (2018)
High-Dimensional Mixed Graphical Models
Jie Cheng et al.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2017)
DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants
Janet Pinero et al.
NUCLEIC ACIDS RESEARCH (2017)
De novo pathway-based biomarker identification
Nicolas Alcaraz et al.
NUCLEIC ACIDS RESEARCH (2017)
A novel FOXA1/ESR1 interacting pathway: A study of Oncomine™ breast cancer microarrays
Sanjib Chaudhary et al.
ONCOLOGY LETTERS (2017)
Integrated Theory- and Data-driven Feature Selection in Gene Expression Data Analysis
Vineet K. Raghu et al.
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017) (2017)
Learning mixed graphical models with separate sparsity parameters and stability-based model selection
Andrew J. Sedgewick et al.
BMC BIOINFORMATICS (2016)
Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer
Guanglong Jiang et al.
BMC GENOMICS (2016)
The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins
Andrew D. Rouillard et al.
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION (2016)
FERAL: network-based classifier with application to breast cancer outcome prediction
Amin Allahyar et al.
BIOINFORMATICS (2015)
An epigenetically distinct breast cancer cell subpopulation promotes collective invasion
Jill M. Westcott et al.
JOURNAL OF CLINICAL INVESTIGATION (2015)
Learning the Structure of Mixed Graphical Models
Jason D. Lee et al.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2015)
Selection and estimation for mixed graphical models
Shizhe Chen et al.
BIOMETRIKA (2015)
STRING v10: protein-protein interaction networks, integrated over the tree of life
Damian Szklarczyk et al.
NUCLEIC ACIDS RESEARCH (2015)
Delineation of a FOXA1/ERα/AGR2 Regulatory Loop That Is Dysregulated in Endocrine Therapy-Resistant Breast Cancer
Tricia M. Wright et al.
MOLECULAR CANCER RESEARCH (2014)
Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables
Bernd Fellinghauer et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2013)
A Sparse-Group Lasso
Noah Simon et al.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2013)
Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis
Christine Staiger et al.
Frontiers in Genetics (2013)
Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions
Yupeng Cun et al.
BMC BIOINFORMATICS (2012)
Conservative independence-based causal structure learning in absence of adjacency faithfulness
Jan Lemeire et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING (2012)
MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibition
Dai Horiuchi et al.
JOURNAL OF EXPERIMENTAL MEDICINE (2012)
The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
Anne-Claire Haury et al.
PLOS ONE (2011)
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome
David Venet et al.
PLOS COMPUTATIONAL BIOLOGY (2011)
Common breast cancer susceptibility alleles are associated with tumour subtypes in BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2
Anna Marie Mulligan et al.
BREAST CANCER RESEARCH (2011)
Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
Marc Johannes et al.
BIOINFORMATICS (2010)
Network-based support vector machine for classification of microarray samples
Yanni Zhu et al.
BMC BIOINFORMATICS (2009)
Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer
Xi Chen et al.
JOURNAL OF COMPUTATIONAL BIOLOGY (2009)
Dynamic modularity in protein interaction networks predicts breast cancer outcome
Ian W. Taylor et al.
NATURE BIOTECHNOLOGY (2009)
Hybrid huberized support vector machines for microarray classification and gene selection
Li Wang et al.
BIOINFORMATICS (2008)
The humoral immune system has a key prognostic impact in node-negative breast cancer
Marcus Schmidt et al.
CANCER RESEARCH (2008)
Sparse inverse covariance estimation with the graphical lasso
Jerome Friedman et al.
BIOSTATISTICS (2008)
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series
Christine Desmedt et al.
CLINICAL CANCER RESEARCH (2007)
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer
Anna V. Ivshina et al.
CANCER RESEARCH (2006)
Incorporating gene functions as priors in model-based clustering of microarray gene expression data
W Pan
BIOINFORMATICS (2006)
Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis
C Sotiriou et al.
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2006)
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
A Subramanian et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)
Online predicted human interaction database
KR Brown et al.
BIOINFORMATICS (2005)
Towards precise classification of cancers based on robust gene functional expression profiles
Z Guo et al.
BMC BIOINFORMATICS (2005)
Gene-expression pro-files to predict distant metastasis of lymph-node-negative primary breast cancer
YX Wang et al.
LANCET (2005)
A general framework for weighted gene co-expression network analysis
Bin Zhang et al.
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY (2005)
Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts
Y Pawitan et al.
BREAST CANCER RESEARCH (2005)
KEGG: Kyoto Encyclopedia of Genes and Genomes
M Kanehisa et al.
NUCLEIC ACIDS RESEARCH (2000)