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Machine learning for the advancement of genome-scale metabolic modeling
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …
interrelations between genotype, phenotype, and external environment. The recent …
Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions
Gene Expression is the process of determining the physical characteristics of living beings
by generating the necessary proteins. Gene Expression takes place in two steps, translation …
by generating the necessary proteins. Gene Expression takes place in two steps, translation …
[HTML][HTML] Deep learning-based prediction of Alzheimer's disease using microarray gene expression data
Alzheimer's disease is a genetically complex disorder, and microarray technology provides
valuable insights into it. However, the high dimensionality of microarray datasets and small …
valuable insights into it. However, the high dimensionality of microarray datasets and small …
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
DL Tong, AC Schierz - Artificial intelligence in medicine, 2011 - Elsevier
OBJECTIVE: Suitable techniques for microarray analysis have been widely researched,
particularly for the study of marker genes expressed to a specific type of cancer. Most of the …
particularly for the study of marker genes expressed to a specific type of cancer. Most of the …
[HTML][HTML] Towards knowledge-based gene expression data mining
The field of gene expression data analysis has grown in the past few years from being
purely data-centric to integrative, aiming at complementing microarray analysis with data …
purely data-centric to integrative, aiming at complementing microarray analysis with data …
Multi-view feature selection for identifying gene markers: a diversified biological data driven approach
Background In recent years, to investigate challenging bioinformatics problems, the
utilization of multiple genomic and proteomic sources has become immensely popular …
utilization of multiple genomic and proteomic sources has become immensely popular …
An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients
R Yang, J **ong, D Deng, Y Wang, H Liu, G Jiang… - Translational …, 2016 - Elsevier
Accumulating evidence shows that clinical factors alone are not adequate for predicting the
survival of patients with ovarian cancer (OvCa), and many genes have been found to be …
survival of patients with ovarian cancer (OvCa), and many genes have been found to be …
Identification and functional assessment of novel gene sets towards better understanding of dysplasia associated oral carcinogenesis
Oral epithelial dysplasia (OED) often precedes oral cancer. Understanding the underlying
complex biological aspects of dysplasia associated oral carcinogenesis using important …
complex biological aspects of dysplasia associated oral carcinogenesis using important …
[PDF][PDF] Relational subgroup discovery for gene expression data mining
We propose a methodology for predictive classification from gene expression data, able to
combine the robustness of highdimensional statistical classification methods with the …
combine the robustness of highdimensional statistical classification methods with the …
Genetic algorithm-neural network: feature extraction for bioinformatics data.
DL Tong - 2010 - eprints.bournemouth.ac.uk
With the advance of gene expression data in the bioinformatics field, the questions which
frequently arise, for both computer and medical scientists, are which genes are significantly …
frequently arise, for both computer and medical scientists, are which genes are significantly …