Machine learning and integrative analysis of biomedical big data
Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …
of massive amounts of omics data from multiple sources: genome, epigenome …
A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: from computational needs to machine learning and deep learning
Biomarkers, also known as biological markers, are substances like transcripts,
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
Short term load forecasting based on feature extraction and improved general regression neural network model
Y Liang, D Niu, WC Hong - Energy, 2019 - Elsevier
Along with the deregulation of electric power market as well as aggregation of renewable
resources, short term load forecasting (STLF) has become more and more momentous …
resources, short term load forecasting (STLF) has become more and more momentous …
Recursive memetic algorithm for gene selection in microarray data
Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a
small subset of genes that enable a classifier to predict the presence or type of disease …
small subset of genes that enable a classifier to predict the presence or type of disease …
Computational techniques and tools for omics data analysis: state-of-the-art, challenges, and future directions
The heterogeneous and high-dimensional nature of omics data presents various challenges
in gaining insights while analysis. In the era of big data, omics data is available as genome …
in gaining insights while analysis. In the era of big data, omics data is available as genome …
Forecasting electricity consumption using a novel hybrid model
GF Fan, X Wei, YT Li, WC Hong - Sustainable Cities and Society, 2020 - Elsevier
In recent years, the electricity industry has become increasingly important to social and
economic development. For sustainability of the power industrial business, an accurate …
economic development. For sustainability of the power industrial business, an accurate …
Comparative analysis of feature selection algorithms for computational personality prediction from social media
With the rapid growth of social media, users are getting involved in virtual socialism,
generating a huge volume of textual and image contents. Considering the contents such as …
generating a huge volume of textual and image contents. Considering the contents such as …
A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis
Recent advancements in biomedical technologies and the proliferation of high-dimensional
Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and …
Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and …
Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer
Background As many complex omics data have been generated during the last two
decades, dimensionality reduction problem has been a challenging issue in better mining …
decades, dimensionality reduction problem has been a challenging issue in better mining …
A review of matched-pairs feature selection methods for gene expression data analysis
With the rapid accumulation of gene expression data from various technologies, eg,
microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry …
microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry …