[HTML][HTML] Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)
Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …
intending the primary solution scheme for the datasets containing one or more missing …
Genomic approaches to posttraumatic stress disorder: the psychiatric genomic consortium initiative
Posttraumatic stress disorder (PTSD) after exposure to a traumatic event is a highly
prevalent psychiatric disorder. Heritability estimates from twin studies as well as from recent …
prevalent psychiatric disorder. Heritability estimates from twin studies as well as from recent …
[HTML][HTML] A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network
J Luo, Q Xiao - Journal of biomedical informatics, 2017 - Elsevier
MicroRNAs (miRNAs) play a critical role by regulating their targets in post-transcriptional
level. Identification of potential miRNA-disease associations will aid in deciphering the …
level. Identification of potential miRNA-disease associations will aid in deciphering the …
Gene ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery
GK Mazandu, ER Chimusa… - Briefings in bioinformatics, 2017 - academic.oup.com
Gene Ontology (GO) semantic similarity tools enable retrieval of semantic similarity scores,
which incorporate biological knowledge embedded in the GO structure for comparing or …
which incorporate biological knowledge embedded in the GO structure for comparing or …
An efficient ensemble method for missing value imputation in microarray gene expression data
X Zhu, J Wang, B Sun, C Ren, T Yang, J Ding - BMC bioinformatics, 2021 - Springer
Background The genomics data analysis has been widely used to study disease genes and
drug targets. However, the existence of missing values in genomics datasets poses a …
drug targets. However, the existence of missing values in genomics datasets poses a …
Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach
Abstract Background Gene Ontology (GO) is one of the most popular bioinformatics
resources. In the past decade, Gene Ontology-based gene semantic similarity has been …
resources. In the past decade, Gene Ontology-based gene semantic similarity has been …
Predicting MicroRNA-disease associations using Kronecker regularized least squares based on heterogeneous omics data
MicroRNAs (miRNAs) play critical roles in many biological processes. Predicting the miRNA-
disease associations will aid in deciphering the underlying pathogenesis of human …
disease associations will aid in deciphering the underlying pathogenesis of human …
Missing value estimation of microarray data using Sim-GAN
Microarray data analysis needs utmost care as it plays a significant role in cancer study. Due
to the excessive complexity of the data extraction process, it loses some relevant information …
to the excessive complexity of the data extraction process, it loses some relevant information …
Microarray missing value imputation: A regularized local learning method
Microarray experiments on gene expression inevitably generate missing values, which
impedes further downstream biological analysis. Therefore, it is key to estimate the missing …
impedes further downstream biological analysis. Therefore, it is key to estimate the missing …
Imputation of gene expression data in blood cancer and its significance in inferring biological pathways
Purpose: Gene expression data generated from microarray technology is often analyzed for
disease diagnostics and treatment. However, this data suffers with missing values that may …
disease diagnostics and treatment. However, this data suffers with missing values that may …