Search Results - integrative autoencoder

scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data by Yin, Qingyang, Wang, Yang, Guan, Jinting, Ji, Guoli

“… Since single-cell gene expression data are high-dimensional and sparse with dropouts, we propose scIAE, an integrative autoencoder-based ensemble classification framework, to firstly perform multiple…”
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AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments by Yu, Tianwei

“… Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME…”
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iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification by Dhillon, Arwinder, Singh, Ashima, Bhalla, Vinod Kumar

“…) for biomarker identification and cancer subtype classification. In this research, iMVAN, an integrated Multimodal Variational Autoencoder and Network fusion, is presented for biomarker discovery and classification of cancer subtypes…”
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An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery by Pyo, JongCheol, Duan, Hongtao, Ligaray, Mayzonee, Kim, Minjeong, Baek, Sangsoo, Kwon, Yong Sung, Lee, Hyuk, Kang, Taegu, Kim, Kyunghyun, Cha, YoonKyung, Cho, Kyung Hwa

“…Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based…”
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Integrative Analysis of Heterogeneous Cancer Data Using Autoencoder Neural Networks by Mia Md Tofayel Gonee Manik

“… Machine learning methods especially autoencoder neural networks show promising results in detecting diagnostic patterns from difficult data…”
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Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model by Su, Lingtao, Xu, Chunhui, Zeng, Shuai, Su, Li, Joshi, Trupti, Stacey, Gary, Xu, Dong

“… To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE…”
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InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data by Bowen, Lei, Guhaniyogi, Rajarshi, Chandra, Krishnendu, Scheffler, Aaron, Mallick, Bani

“…), this article proposes a novel approach, referred to as Integrative Variational Autoencoder (\texttt{InVA}) method, which borrows information from multiple images obtained from different sources to draw predictive inference of an image…”
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Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments by Yu, Tianwei

“… Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME…”
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Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis by Ma, Tianle, Zhang, Aidong

“…Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology…”
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Integrative Multi-Omics Prognostic Modeling of Glioma Recurrence Using Variational Autoencoder and Similarity Network Fusion by Tran, Phuong Lam, Tang, Yun, Hsu, Justin BoKai, Lee, Tzong-Yi

“… Traditional prognostic methods rely on clinical and molecular markers, yet they often fail to leverage the predictive potential of integrative multiomics data…”
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Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping by Madhumita, Paul, Sushmita

“… Over the years, several computational attempts have been made to identify the cancer subtypes accurately using integrative multi-omics analysis…”
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Efficient Masked Autoencoder for Birdsong Representation with Applications on Wild Bird Species Classification by Zhang, Qin, Hu, Shipeng, Wang, Hengrui, Yi, Jizheng, Zhou, Guoxiong, Shen, Xiangyu, Peng, Weixiong, Xiao, Zhishu, Chen, Aibin

“…‐based recognition methods depend on extensive manual data annotation. To address these challenges, we propose Contrastive Residual Masked AutoEncoder‐BirdNET (CResMAE‐BirdNET…”
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Artificial Neural Networks for Short-Form Development of Psychometric Tests: A Study on Synthetic Populations Using Autoencoders by Casella, Monica, Dolce, Pasquale, Ponticorvo, Michela, Milano, Nicola, Marocco, Davide

“… This article proposes a machine learning–based autonomous procedure for short-form development that combines explanatory and predictive techniques in an integrative approach…”
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Accurate noise-robust classification of Bacillus species from MALDI-TOF MS spectra using a denoising autoencoder by Uvarova, Yulia E., Demenkov, Pavel S., Kuzmicheva, Irina N., Venzel, Artur S., Mischenko, Elena L., Ivanisenko, Timofey V., Efimov, Vadim M., Bannikova, Svetlana V., Vasilieva, Asya R., Ivanisenko, Vladimir A., Peltek, Sergey E.

“…). One limitation of such methods is the noise in MS spectra. In this study, we used a denoising autoencoder (DAE…”
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Cancer subtype identification by consensus guided graph autoencoders by Liang, Cheng, Shang, Mingchao, Luo, Jiawei

“… As a result, great computational efforts have been made to accurately identify cancer subtypes via integrative analysis of these multi-omics datasets…”
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A demonstration of unsupervised machine learning in species delimitation by Derkarabetian, Shahan, Castillo, Stephanie, Koo, Peter K., Ovchinnikov, Sergey, Hedin, Marshal

“…•Multiple empirical and simulated datasets demonstrate their utility.•These approaches are versatile, with great potential in integrative species delimitation…”
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Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis by Ma, Tianle, Zhang, Aidong

“…Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology…”
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Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration by Krishnan, Keerthi, Kassab, Ryan, Agajanian, Steve, Verkhivker, Gennady

“…In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based…”
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eVAE: Evolutionary Variational Autoencoder by Wu, Zhangkai, Cao, Longbing, Qi, Lei

“…Variational autoencoders (VAEs) are challenged by the imbalance between representation inference and task fitting caused by surrogate loss…”
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MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder by Yao, Xiaohui, Jiang, Xiaohan, Luo, Haoran, Liang, Hong, Ye, Xiufen, Wei, Yanhui, Cong, Shan

“… Methods We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT…”
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