Search Results - Sparse Convolutional Denoising Autoencoder (SCDA)

TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition.

Authors: Bodapati, Jyostna Devi Balaji, Bharadwaj Bagepalli

Source: Multimedia Tools & Applications; Mar2024, Vol. 83 Issue 8, p22099-22117, 19p

Sparse Convolutional Denoising Autoencoders for Genotype Imputation

Authors: Junjie Chen Xinghua Shi

Source: Genes, Vol 10, Iss 9, p 652 (2019)

Subject Terms: genotype imputation, convolutional neural network, autoencoder, sparse model, deep learning, Genetics, QH426-470

Relation: https://www.mdpi.com/2073-4425/10/9/652; https://doaj.org/toc/2073-4425; https://doaj.org/article/27820baa537147719ce45713616a3bd8

Availability: https://doi.org/10.3390/genes10090652
https://doaj.org/article/27820baa537147719ce45713616a3bd8

Sparse Convolutional Denoising Autoencoders for Genotype Imputation.

Authors: Chen, Junjie Shi, Xinghua

Source: Genes; Sep2019, Vol. 10 Issue 9, p652-652, 1p

Subject Terms: GENOTYPES, MARKOV processes, SPARSE matrices, INFERENTIAL statistics, DEEP learning, LINKAGE (Genetics)

Spatially informed graph transformers for spatially resolved transcriptomics.

Authors: Bao, Xinyu Bai, Xiaosheng Liu, Xiaoping et al.

Source: Communications Biology; 4/4/2025, Vol. 8 Issue 1, p1-11, 11p

Subject Terms: GRAPH neural networks, GENE expression, REPRESENTATIONS of graphs, TRANSFORMER models, BIOCOMPLEXITY

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.

Authors: Hu, Yaofeng Xiao, Kai Yang, Hengyu et al.

Source: Briefings in Bioinformatics; Mar2024, Vol. 25 Issue 2, p1-11, 11p

Subject Terms: TRANSCRIPTOMES, GRAPH neural networks, HETEROGENEITY

An autoencoder-based deep learning method for genotype imputation

Authors: Meng Song Jonathan Greenbaum Joseph Luttrell et al.

Source: Frontiers in Artificial Intelligence, Vol 5 (2022)

Subject Terms: genotype imputation, deep learning, autoencoder, paired sample t-test, GWAS, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/frai.2022.1028978/full; https://doaj.org/toc/2624-8212

Access URL: https://doaj.org/article/1002be0b29d54fc69e4b78b22546b6d0

An autoencoder-based deep learning method for genotype imputation

Authors: Song, Meng Greenbaum, Jonathan Luttrell, Joseph et al.

Subject Terms: Genotype imputation, Deep learning, Autoencoder, Paired sample t-test, GWAS

File Description: 15 pages

Relation: Frontiers Media; http://dx.doi.org/10.3389/frai.2022.1028978; Frontiers in Artificial Intelligence, Vol. 5; Faculty/ Researcher Works; http://hdl.handle.net/20.500.12613/10006

Availability: https://hdl.handle.net/20.500.12613/10006

STICI: Split-Transformer with integrated convolutions for genotype imputation.

Authors: Mowlaei, Mohammad Erfan Li, Chong Jamialahmadi, Oveis et al.

Source: Nature Communications; 2/1/2025, Vol. 16 Issue 1, p1-14, 14p

Subject Terms: GENETIC variation, LIFE sciences, LINKAGE disequilibrium, GENOTYPES, GENOMES

Deep Learning Methods for Omics Data Imputation.

Authors: Huang, Lei Song, Meng Shen, Hui et al.

Source: Biology (2079-7737); Oct2023, Vol. 12 Issue 10, p1313, 26p

Subject Terms: DEEP learning, GENERATIVE adversarial networks, MISSING data (Statistics), TRANSFORMER models, MULTIOMICS

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.

Authors: Wang, Lequn Hu, Yaofeng Xiao, Kai et al.

Source: Briefings in Bioinformatics; Jul2024, Vol. 25 Issue 4, p1-13, 13p

Subject Terms: TRANSCRIPTOMES, GENE expression, BIOLOGICAL systems, MORPHOGENESIS, DATA integration, MOLECULAR pathology

A systematic survey of deep learning in breast cancer.

Authors: Xiang Yu Qinghua Zhou Shuihua Wang et al.

Source: International Journal of Intelligent Systems; Jan2022, Vol. 37 Issue 1, p152-216, 65p

Subject Terms: DEEP learning, BREAST cancer, COMPUTER vision, CONVOLUTIONAL neural networks, CANCER diagnosis, IMAGE analysis

An autoencoder-based deep learning method for genotype imputation

Subject Terms: Genotype imputation, Paired sample t-test, GWAS, Deep learning, Autoencoder

Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network.

Authors: Liu, Shuaiqi Wang, Xu Zhao, Ling et al.

Source: IEEE/ACM Transactions on Computational Biology & Bioinformatics; Sep/Oct2021, Vol. 18 Issue 5, p1710-1721, 12p

Towards an efficient model for network intrusion detection system (IDS): systematic literature review.

Authors: Abdulganiyu, Oluwadamilare Harazeem Tchakoucht, Taha Ait Saheed, Yakub Kayode

Source: Wireless Networks (10220038); Jan2024, Vol. 30 Issue 1, p453-482, 30p

Subject Terms: SOFTWARE engineering, INTRUSION detection systems (Computer security), ARTIFICIAL intelligence, INTERNET security, INTERNET

Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects.

Authors: Guo, Yu Zhang, Jundong Sun, Bin et al.

Source: Sensors (14248220); Aug2023, Vol. 23 Issue 16, p7263, 39p

Subject Terms: DEEP learning, FAULT diagnosis, GENERATIVE adversarial networks, MACHINE learning

231 Anomaly detection for breed purity analysis

Authors: Xiaohan Jiang Juan P Steibel

Source: Journal of Animal Science. 102:24-25

Subject Terms: 0403 veterinary science, 2. Zero hunger, 0402 animal and dairy science, 04 agricultural and veterinary sciences

2021 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 18.

Source: IEEE/ACM Transactions on Computational Biology & Bioinformatics; Jan/Feb2022, Vol. 19 Issue 1, p1-56, 56p

A Review of Integrative Imputation for Multi-Omics Datasets.

Authors: Song, Meng Greenbaum, Jonathan Luttrell IV, Joseph et al.

Source: Frontiers in Genetics; 10/15/2020, Vol. 11, pN.PAG-N.PAG, 15p

Subject Terms: MULTIPLE imputation (Statistics), DEEP learning, INDIVIDUALIZED medicine, MATRIX decomposition, STATISTICS, MACHINE learning

Algorithms in Advanced Artificial Intelligence : ICAAAI-2023

Authors: R. N. V. Jagan Mohan Vasamsetty Chandra Sekhar V. M. N. S. S. V. K. R. Gupta

Resource Type: eBook.

Subjects: Artificial intelligence, Algorithms

Categories: COMPUTERS / Computer Science, COMPUTERS / Business & Productivity Software / Word Processing, COMPUTERS / Data Science / Machine Learning

Congress on Intelligent Systems

Authors: Mukesh Saraswat Harish Sharma K. Balachandran et al.

Resource Type: eBook.

Subjects: Computational intelligence, Artificial intelligence, Robotics

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