Search Results - Variational Autoencoder, The Cancer Genome Atlas

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data by Albaradei, Somayah, Napolitano, Francesco, Thafar, Maha A., Gojobori, Takashi, Essack, Magbubah, Gao, Xin

“…), microRNA sequencing (microRNA-Seq), and DNA methylation data from The Cancer Genome Atlas (TCGA…”
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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders by Way, Gregory P., Greene, Casey S.

“…The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels…”
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Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping by Li, Zuqi, Katz, Sonja, Saccenti, Edoardo, Fardo, David W, Claes, Peter, Martins dos Santos, Vitor A P, Van Steen, Kristel, Roshchupkin, Gennady V

“… Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity…”
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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

“… Multiomics data, including mRNA, miRNA, DNA methylation, and CNV profiles from 512 LGG and 350 GBM patients in The Cancer Genome Atlas (TCGA…”
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Cancer Subtyping Through Multi-Omics Feature Selection: A Review of Methods and the Superiority of Variational Autoencoders by Mukhdoomi, Muneeba Afzal, Chachoo, Manzoor Ahmad

“… However, feature selection poses a challenge. This study leveraged The Cancer Genome Atlas (TCGA…”
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Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data by Franco, Edian F., Rana, Pratip, Cruz, Aline, Calderón, Víctor V., Azevedo, Vasco, Ramos, Rommel T. J., Ghosh, Preetam

“… In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection…”
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Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping by Cai, Yueyi, Wang, Shunfang

“… This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR…”
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(\Gamma\)-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data by Kim, Jason Z, Perrin-Gilbert, Nicolas, Narmanli, Erkan, Klein, Paul, Myers, Christopher R, Cohen, Itai, Waterfall, Joshua J, Sethna, James P

“… For example, despite the tens of thousands of genes in the human genome, the principled study of genomics is fruitful because biological processes rely on coordinated organization that results…”
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SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer by Mora, Ariane, Schmidt, Christina, Balderson, Brad, Frezza, Christian, Bodén, Mikael

“…Background Clear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and…”
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Inferring Personalized and Race-Specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model by Chen, Zhong, Edwards, Andrea, Hicks, Chindo, Zhang, Kun

“…) of each individual PCa patient. The core of the proposed model is a deep variational autoencoder (VAE…”
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A deep imputation and inference framework for estimating personalized and race-specific causal effects of genomic alterations on PSA by Chen, Zhong, Cao, Bo, Edwards, Andrea, Deng, Hongwen, Zhang, Kun

“…Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa…”
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Transfer Learning Leverages Cancer Genomics for In Silico CRISPR/Cas9 Genetic Interaction Screen Surrogate Model by Chang, Daniel

“… Genome-wide CRISPR/Cas9 loss-of-function screens can effectively probe for genetic interactions by identifying differential genetic dependencies between co-isogenic "query mutant" cell lines…”
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Genomic and Imaging Biomarker Analysis in Lung Cancer: A SHAP-Guided Approach by Sathya, A., R, Deevna Reddy, Blessy, J. Jino

“… Gene expression data from The Cancer Genome Atlas (TCGA) is utilized to train Random Forest classifiers for predicting key oncogenic mutation statuses, including EGFR, KRAS, and ALK, achieving an accuracy of 93.5…”
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Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders by Way, Gregory P, Greene, Casey S

“…The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels…”
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Unsupervised spatially embedded deep representation of spatial transcriptomics by Xu, Hang, Fu, Huazhu, Long, Yahui, Ang, Kok Siong, Sethi, Raman, Chong, Kelvin, Li, Mengwei, Uddamvathanak, Rom, Lee, Hong Kai, Ling, Jingjing, Chen, Ao, Shao, Ling, Liu, Longqi, Chen, Jinmiao

“… simultaneously embedded with the corresponding spatial information through a variational graph autoencoder…”
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Evaluating deep variational autoencoders trained on pan-cancer gene expression by Way, Gregory P, Greene, Casey S

“… The Cancer Genome Atlas (TCGA) has released a large compendium of over 10,000 tumors with RNA-seq gene expression measurements…”
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Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function by Eraslan, Gökcen, Drokhlyansky, Eugene, Anand, Shankara, Fiskin, Evgenij, Subramanian, Ayshwarya, Slyper, Michal, Wang, Jiali, Van Wittenberghe, Nicholas, Rouhana, John M, Waldman, Julia, Ashenberg, Orr, Lek, Monkol, Dionne, Danielle, Win, Thet Su, Cuoco, Michael S, Kuksenko, Olena, Tsankov, Alexander M, Branton, Philip A, Marshall, Jamie L, Greka, Anna, Getz, Gad, Segrè, Ayellet V, Aguet, François, Rozenblatt-Rosen, Orit, Ardlie, Kristin G, Regev, Aviv

“…, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder…”
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Deep generative neural network for accurate drug response imputation by Jia, Peilin, Hu, Ruifeng, Pei, Guangsheng, Dai, Yulin, Wang, Yin-Ying, Zhao, Zhongming

“… In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space…”
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Meta Cancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data by Albaradei, Somayah, Napolitano, Francesco, Thafar, Maha A, Gojobori, Takashi, Essack, Magbubah, Gao, Xin

“…), microRNA sequencing (microRNA-Seq), and DNA methylation data from The Cancer Genome Atlas (TCGA…”
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VTrans: A VAE-Based Pre-Trained Transformer Method for Microbiome Data Analysis by Shi, Xinyuan, Zhu, Fangfang, Min, Wenwen

“…Predicting the survival outcomes and assessing the risk of patients play a pivotal role in comprehending the microbial composition across various stages of cancer…”
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