Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma [PDF]
Cytology and Genetics, 2007 To enhance glioblastoma (GB) marker discovery we compared gene expression in GB with human normal brain (NB) by accessing SAGE Genie web site and compared obtained results with published data. Nine GB and five NB SAGE-libraries were analyzed using the Digital Gene Expression Displayer (DGED), the results of DGED were tested by Northern blot analysis ...
Kavsan, V.M. +9 more
openaire +4 more sources
Integrative analysis of large-scale biological data sets [PDF]
, 2011 We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular
Enrico Glaab +2 more
core +2 more sources
Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques [PDF]
Bioinformatics, 2006 Abstract Motivation: We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody–antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic ...
Sundaresh, Suman +7 more
openaire +3 more sources
A temporal precedence based clustering method for gene expression microarray data [PDF]
, 2010 Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to ...
Buchanan-Wollaston, Vicky +2 more
core +4 more sources
Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis [PDF]
, 2011 Background: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis.
Vasile Palade, Zhenyu Wang
core +4 more sources
Use of pre-transformation to cope with outlying values in important candidate genes [PDF]
, 2010 Outlying values in predictors often strongly affect the results of statistical analyses in high-dimensional settings. Although they frequently occur with most high-throughput techniques, the problem is often ignored in the literature. We suggest to use a
Boulesteix, Anne-Laure +2 more
core +1 more source
Automating the processing of cDNA microarray images [PDF]
, 2008 This work is concerned with the development of an automatic image processing tool for DNA microarray images. This paper proposes, implements and tests a new tool for cDNA image analysis.
Goulermas, J. Y. +4 more
core +1 more source
Differential Gene Expression of Human Mast cell Activation Reveals Gene profiles of Innate and Adaptive Immunity. [PDF]
, 2007 High-density oligonucleotide microarray is a promising approach for high throughput analysis. It has been extensively used in many areas of biomedical research.
Alirio Jose Melendez +3 more
core +2 more sources
Visualizing Gene Clusters using Neighborhood Graphs in R [PDF]
, 2008 The visualization of cluster solutions in gene expression data analysis gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.
Leisch, Friedrich, Scharl, Theresa
core +1 more source