Structured sparsity regularization for analyzing high-dimensional omics data
S Vinga - Briefings in Bioinformatics, 2021 - academic.oup.com
The development of new molecular and cell technologies is having a significant impact on
the quantity of data generated nowadays. The growth of omics databases is creating a …
the quantity of data generated nowadays. The growth of omics databases is creating a …
Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization
Cancer classification and feature (gene) selection plays an important role in knowledge
discovery in genomic data. Although logistic regression is one of the most popular …
discovery in genomic data. Although logistic regression is one of the most popular …
A network-based methodology to identify subnetwork markers for diagnosis and prognosis of colorectal cancer
The development of reliable methods for identification of robust biomarkers for complex
diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data …
diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data …
Hybrid L1/2+ 2 method for gene selection in the Cox proportional hazards model
Background and objective An important issue in genomic research is to identify the
significant genes that related to survival from tens of thousands of genes. Although Cox …
significant genes that related to survival from tens of thousands of genes. Although Cox …
An integrative analysis system of gene expression using self-paced learning and SCAD-Net
Background Few proposed gene biomarkers have been satisfactory in clinical applications.
That is mainly due to the small studies sample size. Because of the batch effect, different …
That is mainly due to the small studies sample size. Because of the batch effect, different …
SPLSN: An efficient tool for survival analysis and biomarker selection
In genome research, it is a fundamental issue to identify few but important survival‐related
biomarkers. The Cox model is a widely used survival analysis technique, which is used to …
biomarkers. The Cox model is a widely used survival analysis technique, which is used to …
Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model
BACKGROUND: In genome research, it is particularly important to identify molecular
biomarkers or signaling pathways related to phenotypes. Logistic regression model is a …
biomarkers or signaling pathways related to phenotypes. Logistic regression model is a …
A novel cox proportional hazards model for high-dimensional genomic data in cancer prognosis
The Cox proportional hazards model is a popular method to study the connection between
feature and survival time. Because of the high-dimensionality of genomic data, existing Cox …
feature and survival time. Because of the high-dimensionality of genomic data, existing Cox …
[HTML][HTML] Clinical drug response prediction by using a Lq penalized network-constrained logistic regression method
Background/Aims: One of the most important impacts of personalized medicine is the
connection between patients' genotypes and their drug responses. Despite a series of …
connection between patients' genotypes and their drug responses. Despite a series of …
Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis …
BACKGROUND: Targeted therapy using anti-TNF (tumor necrosis factor) is the first option
for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to …
for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to …