Results 31 to 40 of about 31,256,270 (297)

Discretization of gene expression data revised [PDF]

Briefings in Bioinformatics, 2015
Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference.
Cristian Andrés Gallo, Rocío L. Cecchini, Jessica Andrea Carballido, Sandra Micheletto, Ignacio Ponzoni   +4 more
openaire   +4 more sources

Clustering gene expression data using a diffraction‐inspired framework

BioMedical Engineering OnLine, 2012
Background The recent developments in microarray technology has allowed for the simultaneous measurement of gene expression levels. The large amount of captured data challenges conventional statistical tools for analysing and finding inherent ...
Dinger Steven C, Van Wyk Michael A, Carmona Sergio, Rubin David M   +3 more
doaj   +1 more source

GENE BICLUSTERING ON LARGE DATASETS USING FUZZY C-MEANS CLUSTERING

ICTACT Journal on Soft Computing, 2022
The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the ...
M Ramkumar, J Gowrishankar, V Amirtha Preeya, T Pushpa, T Karthikeyan   +4 more
doaj   +1 more source

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Algorithms, 2019
As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree ...
Angelo Ciaramella, Antonino Staiano
doaj   +1 more source

Validating clustering for gene expression data [PDF]

Bioinformatics, 2001
Abstract Motivation: Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms.
Ka Yee Yeung, David R. Haynor, Walter L. Ruzzo   +2 more
openaire   +4 more sources

VIGLA-M: visual gene expression data analytics

BMC Bioinformatics, 2019
Background The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making.
Ismael Navas-Delgado, José García-Nieto, Esteban López-Camacho, Maciej Rybinski, Rocio Lavado, Miguel Ángel Berciano Guerrero, José F. Aldana-Montes   +6 more
doaj   +1 more source

Screening for coping style increases the power of gene expression studies [PDF]

, 2009
<b>Background:</b> Individuals of many vertebrate species show different stress coping styles and these have a striking influence on how gene expression shifts in response to a variety of challenges.
Pilarczyk, Maciej, Kadri, S., Davinia Morera Capdevila, Felicity A, Capdevila, Davinia Morera, Laia Ribas, Felicity A Huntingford, Huntingford, Felicity A, Simon MacKenzie, Ribas, L., MacKenzie, Simon, Sunil Kadri, Morera Capdevila, Davinia, Ribas, Laia, Pilarczyk, M., Morera, Davinia, MacKenzie, S., Huntingford, F.A., Maciej Pilarczyk, Kadri, Sunil, Capdevila, D.M., Huntingford, Felicity A., Ribas Cabezas, Laia, Capdevila Davinia Morera, Huntingford Felicity A., Pilarczyk Maciej, MacKenzie Simon, Kadri Sunil, Ribas Laia   +28 more
core   +2 more sources

Simcluster: clustering enumeration gene expression data on the simplex space [PDF]

, 2007
Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement.
de B Pereira Carlos A, Varuzza Leonardo, Shmulevich Ilya, Varuzza, Leonardo , Ricardo Z. N. Vêncio, Pereira, Carlos A de B, Carlos Pereira, Brentani, Helena , Vêncio Ricardo ZN, Ricardo Z. N. Vêncio, Brentani Helena, Helena Brentani, Shmulevich, Ilya , Leonardo A. B. Varuzza, Carlos A. De B. Pereira, Vêncio, Ricardo ZN, Ilya Shmulevich, Leonardo Varuzza, Carlos A de B Pereira, Ricardo ZN Vêncio   +19 more
core   +1 more source

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

PLoS Computational Biology, 2016
Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches
Daphne Ezer, Victoria Moignard, Berthold Göttgens, Boris Adryan   +3 more
doaj   +1 more source

Doppelgänger spotting in biomedical gene expression data

iScience, 2022
Summary: Doppelgänger effects (DEs) occur when samples exhibit chance similarities such that, when split across training and validation sets, inflates the trained machine learning (ML) model performance.
Li Rong Wang, Xin Yun Choy, Wilson Wen Bin Goh   +2 more
doaj   +1 more source