[go: up one dir, main page]

Chen et al., 2021 - Google Patents

A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes

Chen et al., 2021

Document ID
9146264191695521190
Author
Chen L
Zhou Y
Publication year
Publication venue
Genes & Genomics

External Links

Snippet

Background Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism. Objective This study aims to detect the …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system

Similar Documents

Publication Publication Date Title
Boulesteix et al. IPF‐LASSO: integrative L1‐penalized regression with penalty factors for prediction based on multi‐omics data
Seidman et al. Rapid, phase-free detection of long identity-by-descent segments enables effective relationship classification
Patil et al. Training replicable predictors in multiple studies
Liang et al. An equivalent measure of partial correlation coefficients for high-dimensional gaussian graphical models
Benner et al. High‐dimensional Cox models: the choice of penalty as part of the model building process
Meira‐Machado et al. Estimation in the progressive illness‐death model: A nonexhaustive review
Wang et al. Penalized count data regression with application to hospital stay after pediatric cardiac surgery
Zhou et al. Analysis of factorial time-course microarrays with application to a clinical study of burn injury
Ma et al. Additive risk models for survival data with high-dimensional covariates
Hess et al. Fisher's combined p-value for detecting differentially expressed genes using Affymetrix expression arrays
Rockova et al. Hierarchical Bayesian formulations for selecting variables in regression models
Shi et al. Integrative analysis of high‐throughput cancer studies with contrasted penalization
Chekouo et al. Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease
Ojavee et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis
Albuquerque et al. Comparative study on the performance of different classification algorithms, combined with pre-and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia
Murphy et al. Bayesian model averaging for selection of a risk prediction model for death within thirty days of discharge: the SILVER-AMI study
Wang et al. Identifying gene‐environment interactions incorporating prior information
Massi et al. Learning high-order interactions for polygenic risk prediction
Ginestet et al. Survival stacking with multiple data types using pseudo-observation-based-AUC loss
Saadati et al. Prediction accuracy and variable selection for penalized cause‐specific hazards models
Wang et al. Overlapping group screening for detection of gene-gene interactions: application to gene expression profiles with survival trait
Chen et al. A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes
Vigorito et al. Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL
Dunkler et al. Gene selection in microarray survival studies under possibly non-proportional hazards
De Hertogh et al. A benchmark for statistical microarray data analysis that preserves actual biological and technical variance