Search Results - "integrative autoencoder"

scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data

Authors: Qingyang Yin Yang Wang Jinting Guan et al.

Source: Briefings in Bioinformatics. 23

Subject Terms: Data Analysis, Sequence Analysis, RNA, Gene Expression Profiling, Exome Sequencing, RNA-Seq, Single-Cell Analysis

Access URL: https://pubmed.ncbi.nlm.nih.gov/34913057

scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data

Authors: Yin, Qingyang Wang, Yang Guan, Jinting et al.

Relation: https://zenodo.org/records/5168428; oai:zenodo.org:5168428; https://doi.org/10.5281/zenodo.5168428

Availability: https://doi.org/10.5281/zenodo.5168428
https://zenodo.org/records/5168428

scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data.

Authors: Yin, Qingyang Wang, Yang Guan, Jinting et al.

Source: Briefings in Bioinformatics; Jan2022, Vol. 23 Issue 1, p1-13, 13p

Subject Terms: RNA sequencing, FEATURE extraction, CELLULAR recognition, GENE expression, CLASSIFICATION, VIDEO compression, CHANNEL coding

Structure-guided integrative soft deep clustering analysis of scRNA-seq and scATAC-seq data.

Authors: Xingzuo, Jiang Chenyuan, Wang Jiaxi, Yao et al.

Source: Frontiers in Microbiology; 2025, p1-12, 12p

Subject Terms: DEVELOPMENTAL biology, FUZZY clustering technique, MACHINE learning, CELL differentiation, CELL populations

A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data.

Authors: Kan, Yulong Wang, Weihao Qi, Yunjing et al.

Source: Quantitative Biology; Jun2025, Vol. 13 Issue 2, p1-19, 19p

Subject Terms: DATA integration, RNA sequencing, TASK analysis, CLUSTER analysis (Statistics), METABOLOMICS

Integration of unpaired single cell omics data by deep transfer graph convolutional network.

Authors: Kan, Yulong Qi, Yunjing Zhang, Zhongxiao et al.

Source: PLoS Computational Biology; 1/16/2025, Vol. 21 Issue 1, p1-21, 21p

Subject Terms: BIOLOGICAL systems, BIOLOGICAL variation, ARTIFICIAL intelligence, DEEP learning, PROTEIN structure

Deep Learning in Single-cell Analysis.

Authors: MOLHO, DYLAN JIAYUAN DING WENZ HUOTANG et al.

Source: ACM Transactions on Intelligent Systems & Technology; Jun2024, Vol. 15 Issue 3, p1-62, 62p

Subject Terms: DEEP learning, MACHINE learning, CONCEPT learning, COMPUTER scientists, BIOLOGISTS

q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.

Authors: Marmarelis, Myrl G. Littman, Russell Battaglin, Francesca et al.

Source: Communications Biology; 4/2/2024, Vol. 7, p1-14, 14p

Subject Terms: TRANSCRIPTOMES, CLINICAL trials, GENE libraries, CYTOLOGY, RNA sequencing, RAS oncogenes

DANCE: a deep learning library and benchmark platform for single-cell analysis.

Authors: Ding, Jiayuan Liu, Renming Wen, Hongzhi et al.

Source: Genome Biology; 3/19/2024, Vol. 25 Issue 1, p1-28, 28p

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.

Authors: Mao, Yuzhen Lin, Yen-Yi Wong, Nelson K Y et al.

Source: Bioinformatics; Feb2024, Vol. 40 Issue 2, p1-8, 8p

Subject Terms: GENE expression profiling, PHENOTYPES, RNA sequencing, COVID-19, DEEP learning, PREDICTION models

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data.

Authors: Yin, Qingyang Chen, Liang

Source: Briefings in Bioinformatics; Jan2024, Vol. 25 Issue 1, p1-12, 12p

Subject Terms: BIOLOGICAL neural networks, RNA sequencing, MACHINE learning

CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server.

Authors: Lyu, Pin Zhai, Yijie Li, Taibo et al.

Source: Bioinformatics; Sep2023, Vol. 39 Issue 9, p1-11, 11p

Subject Terms: INTERNET servers, ANNOTATIONS, DATABASES, SOURCE code, SEQUENCE analysis, DATA analysis

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures.

Authors: Martínez-Enguita, David Dwivedi, Sanjiv K Jörnsten, Rebecka et al.

Source: Briefings in Bioinformatics; Sep2023, Vol. 24 Issue 5, p1-14, 14p

Subject Terms: DISEASE risk factors, DNA methylation, ARTIFICIAL neural networks, SYSTEMIC lupus erythematosus, SPACE (Architecture)

Cell-type annotation with accurate unseen cell-type identification using multiple references.

Authors: Xiong, Yi-Xuan Wang, Meng-Guo Chen, Luonan et al.

Source: PLoS Computational Biology; 6/28/2023, Vol. 19 Issue 6, p1-24, 24p, 1 Diagram, 6 Graphs

Subject Terms: DEEP learning, PHENOMENOLOGICAL biology, RNA sequencing, ANNOTATIONS, COVID-19, SOURCE code

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.

Authors: Setty, Samarth Thonta Scott-Boyer, Marie-Pier Cuppens, Tania et al.

Source: International Journal of Molecular Sciences; Jun2022, Vol. 23 Issue 12, p6792-6792, 12p

Subject Terms: RARE diseases, WHOLE genome sequencing, MACHINE learning

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.

Authors: Wang, Juexin Ma, Anjun Chang, Yuzhou et al.

Source: Nature Communications; 3/25/2021, Vol. 12 Issue 1, p1-11, 11p

Subject Terms: GAUSSIAN mixture models, DEEP learning, ARTIFICIAL neural networks, RNA sequencing, GENE expression, NEURAL development

Cancers in Different Conditions, Cancer Research Methods and Diagnosis: An Interdisciplinary Approach

Authors: Nima Rezaei

Resource Type: eBook.

Categories: MEDICAL / Oncology / General

Digital Technologies and Applications : Proceedings of ICDTA’22, Fez, Morocco, Volume 1

Authors: Saad Motahhir Badre Bossoufi

Resource Type: eBook.

Subjects: Computational intelligence, Engineering—Data processing, Artificial intelligence

Categories: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / General, TECHNOLOGY & ENGINEERING / Engineering (General)