[go: up one dir, main page]

Qu et al., 2024 - Google Patents

A Proposed Weighted Multi-Label Classification Approach for Ancestral Population Identification in Admixed Individuals

Qu et al., 2024

View PDF
Document ID
7069651079500308750
Author
Qu Y
Tran D
Publication year
Publication venue
Procedia Computer Science

External Links

Snippet

Detecting and quantifying admixture in individuals is of primary interest in fields such as genetic epidemiology, population genetics, and forensics. This paper introduces a weighted multi-label Classification method to identify ancestral populations in admixed individuals …
Continue reading at www.sciencedirect.com (PDF) (other versions)

Classifications

    • 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
    • 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
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • 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
    • G06F19/14—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for phylogeny or evolution, e.g. evolutionarily conserved regions determination or phylogenetic tree construction
    • 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
    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • 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
    • G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
    • 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
    • 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
    • G06F17/50—Computer-aided design
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • 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

Similar Documents

Publication Publication Date Title
US12354710B1 (en) Scalable pipeline for local ancestry inference
US10755805B1 (en) Ancestry painting with local ancestry inference
Hassan et al. A review study: computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases
Durand et al. Ancestry composition: a novel, efficient pipeline for ancestry deconvolution
Li et al. iORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide composition
Gaudillo et al. Machine learning approach to single nucleotide polymorphism-based asthma prediction
Schumer et al. Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer
Mourad et al. A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies
WO2022036178A1 (en) Ancestry composition determination
Wu et al. Pairwise kinship testing with microhaplotypes: can advancements be made in kinship inference with these markers?
Pei et al. CLADES: A classification‐based machine learning method for species delimitation from population genetic data
Cline et al. Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants
Durand et al. A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes
Fang et al. A generalizable deep learning framework for inferring fine-scale germline mutation rate maps
Lee et al. Survival prediction and variable selection with simultaneous shrinkage and grouping priors
Chase et al. The role of recombination dynamics in shaping signatures of direct and indirect selection across the Ficedula flycatcher genome
Yang et al. Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses
Qu et al. A Proposed Weighted Multi-Label Classification Approach for Ancestral Population Identification in Admixed Individuals
US20230343413A1 (en) Protein structure-based protein language models
Zhang et al. An extended Tajima’s D neutrality test incorporating SNP calling and imputation uncertainties
Fang et al. A deep learning-based framework for estimating fine-scale germline mutation rates
Hassan et al. Integrated rules classifier for predicting pathogenic non-synonymous single nucleotide variants in human
Yao et al. CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs
Stingo et al. A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data
Mukhopadhyay et al. Variable selection method for quantitative trait analysis based on parallel genetic algorithm