RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes
We propose a generic framework for gene regulatory network (GRN) inference approached
as a feature selection problem. GRNs obtained using Machine Learning techniques are …
as a feature selection problem. GRNs obtained using Machine Learning techniques are …
[PDF][PDF] Optimization of clustering algorithms for gene expression data analysis using distance measures
Clustering is one of the fundamental processes of analyzing gene expression data, basically
by comparing gene expression profiles or sample expression profiles. Comparing …
by comparing gene expression profiles or sample expression profiles. Comparing …
Computational approaches to discover and characterize transcription regulatory complex binding from protein-binding microarray-based experiments
D Bray - 2020 - search.proquest.com
Gene regulation is controlled by DNA-bound complexes of transcription factors (TFs) and
indirectly recruited transcriptional cofactors (COFs). Understanding how and where these TF …
indirectly recruited transcriptional cofactors (COFs). Understanding how and where these TF …
[PDF][PDF] Supplementary Information RGBM: Regularized Gradient Boosting Machines for the Identification of Transcriptional Regulators of Discrete Glioma Subtypes
R Mall - 2017 - researchgate.net
Supplementary Information RGBM: Regularized Gradient Boosting Machines for the
Identification of Transcriptional Regulators of D Page 1 Supplementary Information RGBM …
Identification of Transcriptional Regulators of D Page 1 Supplementary Information RGBM …
[PDF][PDF] Year of Publication: 2016
Clustering is one of the fundamental processes of analyzing gene expression data, basically
by comparing gene expression profiles or sample expression profiles. Comparing …
by comparing gene expression profiles or sample expression profiles. Comparing …