[HTML][HTML] Artificial intelligence empowered digital health technologies in cancer survivorship care: A sco** review
L Pan, X Wu, Y Lu, H Zhang, Y Zhou, X Liu, S Liu… - Asia-Pacific Journal of …, 2022 - Elsevier
Objective The objectives of this systematic review are to describe features and specific
application scenarios for current cancer survivorship care services of Artificial intelligence …
application scenarios for current cancer survivorship care services of Artificial intelligence …
An oversampling framework for imbalanced classification based on Laplacian eigenmaps
Imbalanced classification is a challenging problem in machine learning and data mining.
Oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE) …
Oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE) …
Detecting interactive gene groups for single-cell RNA-Seq data based on co-expression network analysis and subgraph learning
High-throughput sequencing technologies have enabled the generation of single-cell RNA-
seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation …
seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation …
Adaptive unsupervised feature learning for gene signature identification in non-small-cell lung cancer
Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer, which
accounts for a proportion of nearly 85%. The increasing availability of genome-wide gene …
accounts for a proportion of nearly 85%. The increasing availability of genome-wide gene …
Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma
Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three
subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and …
subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and …
[HTML][HTML] Integration of deep and ensemble learning for detecting covid-19 in computed tomography images
A Haidar, L Holloway - 2020 - europepmc.org
This paper presents an approach for detecting covid-19 in Computed Tomography (CT)
images by integrating deep convolutional neural networks and ensembles of decision trees …
images by integrating deep convolutional neural networks and ensembles of decision trees …
Scaling Method for Batch Effect Correction of Gene Expression Data Based on Spectral Clustering
Background: Batch effects are usually introduced in gene expression data, which can
dramatically reduce the accuracy of statistical inference in the genomic analysis since …
dramatically reduce the accuracy of statistical inference in the genomic analysis since …