[HTML][HTML] Machine learning-based approach: Global trends, research directions, and regulatory standpoints
The field of machine learning (ML) is sufficiently young that it is still expanding at an
accelerating pace, lying at the crossroads of computer science and statistics, and at the core …
accelerating pace, lying at the crossroads of computer science and statistics, and at the core …
Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review
Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …
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 …
Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients
L Mizrahi, A Choudhary, P Ofer, G Goldberg… - Molecular …, 2023 - nature.com
Bipolar disorder (BD) is a neuropsychiatric mood disorder manifested by recurrent episodes
of mania and depression. More than half of BD patients are non-responsive to lithium, the …
of mania and depression. More than half of BD patients are non-responsive to lithium, the …
An ensemble feature selection approach for analysis and modeling of transcriptome data in alzheimer's disease
Data-driven analysis and characterization of molecular phenotypes comprises an efficient
way to decipher complex disease mechanisms. Using emerging next generation …
way to decipher complex disease mechanisms. Using emerging next generation …
Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma
Y Fu, A Si, X Wei, X Lin, Y Ma, H Qiu, Z Guo, Y Pan… - BMC genomics, 2023 - Springer
Background Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence
within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in …
within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in …
Improving the classification of alzheimer's disease using hybrid gene selection pipeline and deep learning
Alzheimer's is a progressive, irreversible, neurodegenerative brain disease. Even with
prominent symptoms, it takes years to notice, decode, and reveal Alzheimer's. However …
prominent symptoms, it takes years to notice, decode, and reveal Alzheimer's. However …
A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data
Background The discovery of critical biomarkers is significant for clinical diagnosis, drug
research and development. Researchers usually obtain biomarkers from microarray data …
research and development. Researchers usually obtain biomarkers from microarray data …
Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets
Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is
challenging to predict in early drug development stages. Preclinical animal studies often fail …
challenging to predict in early drug development stages. Preclinical animal studies often fail …
Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress
Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy
damage leading to yield reduction in maize. Therefore, the identification of resistant genes …
damage leading to yield reduction in maize. Therefore, the identification of resistant genes …