Hajihosseini et al., 2025 - Google Patents
A comprehensive performance comparison study of various statistical models that accommodate challenges of the gut microbiome dataHajihosseini et al., 2025
- Document ID
- 13890510760661378710
- Author
- Hajihosseini M
- Amini P
- Saidi-Mehrabad A
- Hajizadeh N
- Kozyrskyj A
- Dinu I
- Publication year
- Publication venue
- Statistics in Biosciences
External Links
Snippet
The human gut microbiome refers to trillions of symbiotic bacteria that colonize the human gut after birth, having an essential role in maintaining human health. Various factors can influence the human microbiome, delaying normal gut microbiota's maturation and leading …
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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3431—Calculating a health index for the patient, e.g. for risk assessment
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
-
- 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
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shenhav et al. | FEAST: fast expectation-maximization for microbial source tracking | |
| Alcazar et al. | The association between early-life gut microbiota and childhood respiratory diseases: a systematic review | |
| Munafò et al. | Triangulating evidence through the inclusion of genetically informed designs | |
| Calle et al. | coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies | |
| Stewart et al. | Temporal development of the gut microbiome in early childhood from the TEDDY study | |
| Pechal et al. | A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition | |
| Wallen | Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing | |
| Volant et al. | SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis | |
| Vandeputte et al. | Quantitative microbiome profiling links gut community variation to microbial load | |
| Kemppainen et al. | Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes | |
| Plantinga et al. | MiRKAT-S: a community-level test of association between the microbiota and survival times | |
| Palchak et al. | A decision rule for identifying children at low risk for brain injuries after blunt head trauma | |
| US11621055B2 (en) | Microorganism-related significance index metrics | |
| JP2020530931A (en) | Disease-related microbiome feature analysis process | |
| US20210174967A1 (en) | Disease evaluation index calculation method, apparatus, system, and program, and model creation method for calculating disease evaluation index | |
| Lee et al. | BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease | |
| Streit et al. | Microbiome profiles are associated with cognitive functioning in 45-month-old children | |
| Krautenbacher et al. | A strategy for high‐dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors | |
| Eck et al. | Interpretation of microbiota-based diagnostics by explaining individual classifier decisions | |
| Sovio et al. | Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk | |
| US20200381089A1 (en) | System and method of data interpretation and providing recommendations to the user on the basis of his genetic data and data on the composition of gut microbiota | |
| Guccione et al. | Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data | |
| Tian et al. | Maternal socioeconomic mobility and preterm delivery: a latent class analysis | |
| Vänni et al. | Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome | |
| Ogata et al. | Genetic factors explain the association between pain catastrophizing and chronic widespread pain |