Search Results - stacked sparse autoencoder((suae OR stad))

VTrans: A VAE-Based Pre-Trained Transformer Method for Microbiome Data Analysis.

Authors: Shi, Xinyuan Zhu, Fangfang Min, Wenwen

Source: Journal of Computational Biology; Sep2025, Vol. 32 Issue 9, p850-864, 15p

Improving the generalization of unsupervised feature learning by using data from different sources on gene expression data for cancer diagnosis.

Authors: Liu, Zhen Wang, Ruoyu Zhang, Wenbin

Source: Medical & Biological Engineering & Computing; Apr2022, Vol. 60 Issue 4, p1055-1073, 19p, 1 Color Photograph, 3 Diagrams, 3 Charts, 10 Graphs

SAE-GAN: integrating stacked attention autoencoder and generative adversarial networks for multivariate time series anomaly detection.

Authors: Sun, Shimin Liu, Xiangyun Han, Li et al.

Source: Applied Intelligence; Nov2025, Vol. 55 Issue 16, p1-18, 18p

Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks.

Authors: Azar, Joseph Al Saleh, Mohammed Couturier, Raphaël et al.

Source: Internet of Things (IoT); Jun2025, Vol. 6 Issue 2, p22-N.PAG, 22p

Subject Terms: COMPUTER network traffic, TEXT mining, INDUSTRIAL controls manufacturing, AUTOENCODERS, ANOMALY detection (Computer security), DEEP learning

STAD-GAN: Unsupervised Anomaly Detection on Multivariate Time Series with Self-training Generative Adversarial Networks.

Authors: ZHIJIE ZHANG WENZHONG LI WANGXIANG DING et al.

Source: ACM Transactions on Knowledge Discovery from Data; Jun2023, Vol. 17 Issue 5, p1-18, 18p

Subject Terms: GENERATIVE adversarial networks, ANOMALY detection (Computer security), PROBABILISTIC generative models, INFORMATION technology, MANUFACTURING processes, DEEP learning

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities.

Authors: Yang, Hanchen Cao, Jiannong Li, Wengen et al.

Source: ACM Transactions on Knowledge Discovery from Data; Aug2025, Vol. 19 Issue 7, p1-47, 47p

Subject Terms: MARINE sciences, DATA analysis, NATURAL disaster warning systems, SPATIOTEMPORAL processes, CLIMATE change forecasts, PATTERN perception, OUTLIER detection, DATA quality

Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning.

Authors: Zhang, Huibo Chen, Lulu Li, Lan et al.

Source: NPJ Precision Oncology; 3/19/2025, Vol. 9 Issue 1, p1-15, 15p

Subject Terms: MEDICAL sciences, TUMOR-infiltrating immune cells, TUMOR microenvironment, TUMOR classification, CANCER invasiveness

Classification of lung cancer severity using gene expression data based on deep learning.

Authors: Nassif, Ali Bou Abujabal, Nour Ayman Omar, Aya Alchikh

Source: BMC Medical Informatics & Decision Making; 5/14/2025, Vol. 25 Issue 1, p1-19, 19p

Subject Terms: CONVOLUTIONAL neural networks, FEATURE selection, MEDICAL sciences, LUNG cancer, MACHINE learning, DEEP learning

The current landscape of artificial intelligence in computational histopathology for cancer diagnosis.

Authors: Tiwari, Aaditya Ghose, Aruni Hasanova, Maryam et al.

Source: Discover Oncology; 4/1/2025, Vol. 16 Issue 1, p1-25, 25p

Subject Terms: ARTIFICIAL intelligence, HISTOPATHOLOGY, DIAGNOSTIC services, PATTERN perception, MULTISENSOR data fusion, DEEP learning, CANCER diagnosis

DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs.

Authors: Xinyang Qian Guang Yang Fan Li et al.

Source: Frontiers in Immunology; 2024, p1-14, 14p

Subject Terms: T cell receptors, EARLY detection of cancer, GASTROINTESTINAL cancer, LEARNING strategies, STATISTICAL correlation, CANCER education

ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes.

Authors: Liu, Qingchun Song, Kai

Source: Briefings in Bioinformatics; Jul2023, Vol. 24 Issue 4, p1-8, 8p

Subject Terms: MULTIOMICS, DEEP learning, NUCLEOTIDE sequencing, SURVIVAL analysis (Biometry), PROGNOSTIC models, CANCER prognosis, CANCER patients

Identification of subtypes in digestive system tumors based on multi-omics data and graph convolutional network.

Authors: Zhou, Lin Wang, Ning Zhu, Zhengzhi et al.

Source: International Journal of Machine Learning & Cybernetics; Sep2024, Vol. 15 Issue 9, p3567-3577, 11p

Fuzzy Typological (Re)arrangement: a Prototype of Rethinking the Typology of Roman Tablewares from Sagalassos, Southwest Anatolia.

Authors: Kafetzaki, Danai Poblome, Jeroen Aerts, Jan

Source: Journal of Archaeological Method & Theory; Sep2024, Vol. 31 Issue 3, p707-760, 54p

Subject Terms: VISUAL analytics, FUZZY logic, POTTERY, INFORMATION resources, AMBIVALENCE

Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging.

Authors: Almutiri, Talal Morizig Alomar, Khalid Hamad Alganmi, Nofe Ateq

Source: Applied Sciences (2076-3417); Jul2024, Vol. 14 Issue 13, p5660, 24p

Subject Terms: MULTIOMICS, STANDARD deviations, PEARSON correlation (Statistics), FEATURE selection

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Authors: Kim, Jaeyoon Seok, Junhee

Source: Briefings in Bioinformatics; Jul2024, Vol. 25 Issue 4, p1-12, 12p

Subject Terms: GENERATIVE adversarial networks, GENE expression, MACHINE learning, DEEP learning, DATA distribution

(CDRGI)-Cancer detection through relevant genes identification.

Authors: Al-Obeidat, Feras Rocha, Álvaro Akram, Maryam et al.

Source: Neural Computing & Applications; Jun2022, Vol. 34 Issue 11, p8447-8454, 8p

Subject Terms: NUCLEOTIDE sequencing, RENAL cancer, SUPPORT vector machines, RNA sequencing, SKIN cancer, GENE expression profiling, CANCER cells

A pan-cancer somatic mutation embedding using autoencoders.

Authors: Palazzo, Martin Beauseroy, Pierre Yankilevich, Patricio

Source: BMC Bioinformatics; 12/11/2019, Vol. 20 Issue 1, p1-10, 10p, 2 Diagrams, 1 Chart, 2 Graphs

Subject Terms: SOMATIC mutation, HIERARCHICAL clustering (Cluster analysis), NATURAL language processing, SUPPORT vector machines, DIFFERENTIAL evolution, TUMOR classification

A snapshot neural ensemble method for cancer-type prediction based on copy number variations.

Authors: Karim, Md. Rezaul Rahman, Ashiqur Jares, João Bosco et al.

Source: Neural Computing & Applications; Oct2020, Vol. 32 Issue 19, p15281-15299, 19p

Subject Terms: FORECASTING, CANCER genes, ONCOGENES, NEURAL codes, CANCER patients, CANCER diagnosis, CANCER prognosis

Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model.

Authors: Reyes, Óscar Pérez, Eduardo

Source: ACM Transactions on Intelligent Systems & Technology; Dec2023, Vol. 14 Issue 6, p1-21, 21p

Subject Terms: CANCER diagnosis, RECURRENT neural networks

Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.

Authors: Khadirnaikar, Seema Shukla, Sudhanshu Prasanna, S. R. M.

Source: PLoS ONE; 10/19/2023, Vol. 18 Issue 10, p1-21, 21p

Subject Terms: MULTIOMICS, MACHINE learning, DNA methylation, PROGRESSION-free survival, PROTEIN expression, IDENTIFICATION