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 …
A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings
E Harirchian, SEA Hosseini, K Jadhav, V Kumari… - Journal of Building …, 2021 - Elsevier
Seismic vulnerability assessment of existing buildings is of great concern around the world.
Different countries develop various approaches and methodologies to overcome the …
Different countries develop various approaches and methodologies to overcome the …
Feature selection with multi-class logistic regression
Feature selection can help to reduce data redundancy and improve algorithm performance
in actual tasks. Most of the embedded feature selection models are constructed based on …
in actual tasks. Most of the embedded feature selection models are constructed based on …
Fault diagnosis of wafer acceptance test and chip probing between front-end-of-line and back-end-of-line processes
SKS Fan, CW Cheng, DM Tsai - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the rapid development of the semiconductor industry, fault diagnosis is an important
task in routine operations to determine the root cause for faults that occur. A tool in …
task in routine operations to determine the root cause for faults that occur. A tool in …
A k-nearest neighbours based ensemble via optimal model selection for regression
Ensemble methods based on-NN models minimise the effect of outliers in a training dataset
by searching groups of the closest data points to estimate the response of an unseen …
by searching groups of the closest data points to estimate the response of an unseen …
[HTML][HTML] Gene selection and classification of microarray gene expression data based on a new adaptive L1-norm elastic net penalty
The removal of irrelevant and insignificant genes has always been a major step in
microarray data analysis. The application of gene selection methods in biological datasets …
microarray data analysis. The application of gene selection methods in biological datasets …
[HTML][HTML] Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression
In this work, we provide a computational method of regularized logistic regression for
discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data …
discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data …
Multi-label feature selection based on logistic regression and manifold learning
Like traditional single-label learning, multi-label learning is also faced with the problem of
dimensional disaster. Feature selection is an effective technique for dimensionality reduction …
dimensional disaster. Feature selection is an effective technique for dimensionality reduction …
Drift limit state predictions of rectangular reinforced concrete columns with superelastic shape memory alloy rebars
This study derives empirical drift limit state expressions for rectangular concrete columns
reinforced with nickel–titanium shape memory alloy (SMA) bars using numerical simulation …
reinforced with nickel–titanium shape memory alloy (SMA) bars using numerical simulation …
Multi-view based integrative analysis of gene expression data for identifying biomarkers
The widespread applications in microarray technology have produced the vast quantity of
publicly available gene expression datasets. However, analysis of gene expression data …
publicly available gene expression datasets. However, analysis of gene expression data …