Cargando…

Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning

BACKGROUND: Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, ex...

Descripción completa

Detalles Bibliográficos
Autores principales:	Huang, Xiaoyan, Liu, Hankui, Li, Xinming, Guan, Liping, Li, Jiankang, Tellier, Laurent Christian Asker M., Yang, Huanming, Wang, Jian, Zhang, Jianguo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2018
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763548/ https://www.ncbi.nlm.nih.gov/pubmed/29320986 http://dx.doi.org/10.1186/s12883-017-1010-3

Ejemplares similares

Deep whole-genome sequencing of 90 Han Chinese genomes
por: Lan, Tianming, et al.
Publicado: (2017)

Correction to: Deep whole-genome sequencing of 90 Han Chinese Genomes
por: Lan, Tianming, et al.
Publicado: (2019)

Next-generation sequencing reveals a novel NDP gene mutation in a Chinese family with Norrie disease
por: Huang, Xiaoyan, et al.
Publicado: (2017)

Integrative genetic and single cell RNA sequencing analysis provides new clues to the amyotrophic lateral sclerosis neurodegeneration
por: Liu, Hankui, et al.
Publicado: (2023)

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder
por: Garbulowski, Mateusz, et al.
Publicado: (2021)

Application of Whole Exome Sequencing in Six Families with an Initial Diagnosis of Autosomal Dominant Retinitis Pigmentosa: Lessons Learned
por: Almoguera, Berta, et al.
Publicado: (2015)

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning
por: Nieuwkoop, Thijs, et al.
Publicado: (2023)

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
por: Huang, Yanting, et al.
Publicado: (2021)

Whole-exome sequencing reveals novel mutations and epigenetic regulation in hypopharyngeal carcinoma
por: Wu, Ping, et al.
Publicado: (2017)

Comparison of machine learning approaches for enhancing Alzheimer’s disease classification
por: Li, Qi, et al.
Publicado: (2021)

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer’s Disease Spectrum: Development of the Classifier and Longitudinal Evaluation
por: Lee, Jin San, et al.
Publicado: (2018)

Joint inference of evolutionary transitions to self-fertilization and demographic history using whole-genome sequences
por: Strütt, Stefan, et al.
Publicado: (2023)

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy
por: Mikolas, Pavol, et al.
Publicado: (2018)

Machine learning-based solution reveals cuproptosis features in inflammatory bowel disease
por: Liu, Le, et al.
Publicado: (2023)

Two neuroanatomical subtypes of males with autism spectrum disorder revealed using semi-supervised machine learning
por: Liu, Guanlu, et al.
Publicado: (2022)

Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning
por: Zhu, Qixiao, et al.
Publicado: (2022)

Unexpected Actors in Inflammatory Bowel Disease Revealed by Machine Learning from Whole-Blood Transcriptomic Data
por: Nowak, Jan K., et al.
Publicado: (2022)

Comparison of 16S and whole genome dog microbiomes using machine learning
por: Lewis, Scott, et al.
Publicado: (2021)

Whole-exome sequencing reveals the etiology of the rare primary hepatic mucoepidermoid carcinoma
por: Hou, Ping, et al.
Publicado: (2021)

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease
por: Muñoz-Castro, Clara, et al.
Publicado: (2022)

Detection of Alzheimer’s disease by displacement field and machine learning
por: Zhang, Yudong, et al.
Publicado: (2015)

An interpretable machine learning model for diagnosis of Alzheimer's disease
por: Das, Diptesh, et al.
Publicado: (2019)

Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
por: Fisher, Charles K., et al.
Publicado: (2019)

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease
por: Chang, Chun-Hung, et al.
Publicado: (2021)

Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease
por: Arjaria, Siddhartha Kumar, et al.
Publicado: (2022)

Prediction of Alzheimer’s in People with Coronavirus Using Machine Learning
por: Mohammadi, Shahriar, et al.
Publicado: (2023)

Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences
por: Liu, Zhichang, et al.
Publicado: (2020)

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts
por: Guan, Renchu, et al.
Publicado: (2019)

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data
por: Li, Ziyi, et al.
Publicado: (2021)

Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models
por: Sheng, Yang, et al.
Publicado: (2019)

Genetic architecture, demographic history, and genomic differentiation of Populus davidiana revealed by whole‐genome resequencing
por: Hou, Zhe, et al.
Publicado: (2020)

Whole-exome sequencing reveals the mutational spectrum of testicular germ cell tumours
por: Litchfield, Kevin, et al.
Publicado: (2015)

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study
por: Fu, Liping, et al.
Publicado: (2020)

Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease
por: Naik, Binny, et al.
Publicado: (2020)

Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
por: Rodriguez, Steve, et al.
Publicado: (2021)

The development of an automated machine learning pipeline for the detection of Alzheimer’s Disease
por: Chedid, Nicholas, et al.
Publicado: (2022)

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression
por: Yuan, Chenxi, et al.
Publicado: (2023)

Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment
por: Guo, Liquan, et al.
Publicado: (2022)

Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection
por: Rajab, Mohammed D., et al.
Publicado: (2023)

Predicting sporadic Alzheimer’s disease progression via inherited Alzheimer’s disease-informed machine-learning
por: Franzmeier, Nicolai, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_h2odvm91fc6j26b3jpse2jet0e