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Found 6 papers, 0 papers with code
Novel Modelling Strategies for High-frequency Stock Trading Data
Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods.
A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data
Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes.
Multi-Stage Graph Peeling Algorithm for Probabilistic Core Decomposition
To make the previous PA focus more on dense subgraphs, we propose a multi-stage graph peeling algorithm (M-PA) that has a two-stage data screening procedure added before the previous PA. After removing vertices from the graph based on the user-defined thresholds, we can reduce the graph complexity largely and without affecting the vertices in subgraphs that we are interested in.
Handling highly correlated genes in prediction analysis of genomic studies
Method: We propose a grouping algorithm, which treats highly correlated genes as a group and uses their common pattern to represent the group's biological signal in feature selection.
Simultaneous prediction of multiple outcomes using revised stacking algorithms
Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient.
Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies
Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs).
