Approaches to dimensionality reduction in proteomic biomarker studies
Mass-spectra based proteomic profiles have received widespread attention as potential
tools for biomarker discovery and early disease diagnosis. A major data-analytical problem …
tools for biomarker discovery and early disease diagnosis. A major data-analytical problem …
Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique
These days, microarray gene expression data are playing an essential role in cancer
classifications. However, due to the availability of small number of effective samples …
classifications. However, due to the availability of small number of effective samples …
Random subspace method for multivariate feature selection
In a growing number of domains data captured encapsulates as many features as possible.
This poses a challenge to classical pattern recognition techniques, since the number of …
This poses a challenge to classical pattern recognition techniques, since the number of …
Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields
Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately,
the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging …
the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging …
Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer
J Hannemann, HM Oosterkamp, CAJ Bosch… - Journal of clinical …, 2005 - ascopubs.org
Purpose At present, clinically useful markers predicting response of primary breast
carcinomas to either doxorubicin-cyclophosphamide (AC) or doxorubicin-docetaxel (AD) are …
carcinomas to either doxorubicin-cyclophosphamide (AC) or doxorubicin-docetaxel (AD) are …
MGRFE: multilayer recursive feature elimination based on an embedded genetic algorithm for cancer classification
C Peng, X Wu, W Yuan, X Zhang… - … /ACM transactions on …, 2019 - ieeexplore.ieee.org
Microarray gene expression data have become a topic of great interest for cancer
classification and for further research in the field of bioinformatics. Nonetheless, due to the …
classification and for further research in the field of bioinformatics. Nonetheless, due to the …
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background Gene selection is an important step when building predictors of disease state
based on gene expression data. Gene selection generally improves performance and …
based on gene expression data. Gene selection generally improves performance and …
Cancer classification from gene expression data by NPPC ensemble
The most important application of microarray in gene expression analysis is to classify the
unknown tissue samples according to their gene expression levels with the help of known …
unknown tissue samples according to their gene expression levels with the help of known …
Dissimilarity-based ensembles for multiple instance learning
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather
than individual feature vectors. In this paper, we address the problem of how these bags can …
than individual feature vectors. In this paper, we address the problem of how these bags can …
Selecting useful groups of features in a connectionist framework
Suppose for a given classification or function approximation (FA) problem data are collected
using sensors. From the output of the th sensor, features are extracted, thereby generating …
using sensors. From the output of the th sensor, features are extracted, thereby generating …