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A Deep Variational Approach to Clustering Survival Data [article]

Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt
2022 arXiv   pre-print
We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference.  ...  In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times.  ...  HGG, Hemodialysis, and in-house PET/CT data could not be published due to medical confidentiality. The code is publicly available at https: //github.com/i6092467/vadesc.  ... 
arXiv:2106.05763v3 fatcat:xihbv3kojje27f63sv7uwwin5q

A deep learning approach for uncovering lung cancer immunome patterns [article]

Moritz Hess, Stefan Lenz, Harald Binder
2018 bioRxiv   pre-print
We also propose a sampling-based approach that smooths the original data according to a trained DBM and can be used for visualization and clustering.  ...  We adapt a deep learning approach, deep Boltzmann machines (DBMs), for modeling immune cell gene expression patterns in lung adenocarcinoma.  ...  To 213 be able to select a good deep Boltzmann machine, we introduced a visualization-based 214 approach.  ... 
doi:10.1101/291047 fatcat:foyj3vuok5dctfoav3b4zfl6wy

Multi-omic and multi-view clustering algorithms: review and cancer benchmark [article]

Nimrod Rappoport, Ron Shamir
2018 bioRxiv   pre-print
Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges.  ...  Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data.  ...  Acknowledgements The results published here are based upon data generated by The Cancer Genome Atlas managed by the NCI and NHGRI. Information about TCGA can be found at http://cancergenome.nih.gov.  ... 
doi:10.1101/371120 fatcat:xvteh7ld7ffgbmmer76xkn4v4m

Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

Edian F Franco, Pratip Rana, Aline Cruz, Víctor V Calderón, Vasco Azevedo, Rommel T J Ramos, Preetam Ghosh
2021 Cancers  
Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders.  ...  Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping.  ...  Finally, we run a survival analysis of the identified clusters to validate the results. In Zhang et al. [19] , the authors used a variational autoencoder to integrate multi-omic cancer data.  ... 
doi:10.3390/cancers13092013 pmid:33921978 fatcat:f5aiav3b4jgftgpcxlnbnonouu

Survival-oriented embeddings for improving accessibility to complex data structures [article]

Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer
2021 arXiv   pre-print
Deep learning excels in the analysis of unstructured data and recent advancements allow to extend these techniques to survival analysis.  ...  We propose a hazard-regularized variational autoencoder that supports straightforward interpretation of deep neural architectures in the context of survival analysis, a field highly relevant in healthcare  ...  We thank the anonymous reviewers for their constructive comments, which helped us to improve the manuscript.  ... 
arXiv:2110.11303v2 fatcat:4qpgfhznqbhppm6safqkkrlare

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Giovanna Nicora, Francesca Vitali, Arianna Dagliati, Nophar Geifman, Riccardo Bellazzi
2020 Frontiers in Oncology  
The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization.  ...  The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework.  ...  ACKNOWLEDGMENTS We would like to acknowledge Simone Marini for his valuable help in the initial phases of the study.  ... 
doi:10.3389/fonc.2020.01030 pmid:32695678 pmcid:PMC7338582 fatcat:wr3auiukhrdm7o76ksgayy4aim

A Survey Of Neural Network-based Cancer Prediction Models From Microarray Data

Maisa Daoud, Michael Mayo
2019 Artificial Intelligence in Medicine  
cancer type or the survivability risk; or for clustering unlabeled samples.  ...  Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer,  ...  From a machine learning perspective, survival analysis is a ranking problem in which data points are ranked on their survival times rather than predicting the actual survival times [64] .  ... 
doi:10.1016/j.artmed.2019.01.006 pmid:30797633 fatcat:yuxczrqxhzcyho3a7h32vcrpi4

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model

Hai Yang, Qiang Wei, Dongdong Li, Zhe Wang, Anna R. Panchenko
2020 PLoS Computational Biology  
In this study, based on the deep adversarial learning, we present an end-to-end approach ClusterATAC to leverage high-dimensional features and explore the classification results.  ...  Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the  ...  Moreover, the deep learning approach can also find some heterogeneous clusters that correspond to multiple cancer types.  ... 
doi:10.1371/journal.pcbi.1008405 pmid:33166290 fatcat:w767ufm33zaefdkwovryciwzvi

Deep Learning based multi-omics integration robustly predicts survival in liver cancer [article]

Kumardeep Chaudhary, Olivier B. Poirion, Liangqun Lu, Lana X. Garmire
2017 bioRxiv   pre-print
To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts.  ...  This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients.  ...  To compute the C-index, we first built a Cox-PH model using the training set (cluster labels and survival data) and predict survival using the labels of the test/confirmation set.  ... 
doi:10.1101/114892 fatcat:sezczlvwyneolea7eetkctz5iu

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: an application to Breast Cancer time to diagnosis [article]

Michela Carlotta Massi, Lorenzo Dominoni, Francesca Ieva, Giovanni Fiorito
2022 bioRxiv   pre-print
To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined  ...  We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways  ...  the data of the BC study nested in the cohort.  ... 
doi:10.1101/2022.02.25.481911 fatcat:e2vaivxucbfk3ocl2vtmlewooi

Partitioned learning of deep Boltzmann machines for SNP data [article]

Moritz Hess, Stefan Lenz, Tamara Blaette, Lars Bullinger, Harald Binder
2016 bioRxiv   pre-print
The proposed approach identified three SNPs that seem to jointly influence survival in a validation data set.  ...  After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by  ...  Acknowledgements The results shown here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.  ... 
doi:10.1101/095638 fatcat:udnog3jjrretfk2ekybfm3sh3q

Survival Cluster Analysis [article]

Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin, Ricardo Henao
2020 arXiv   pre-print
In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations  ...  Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their insightful comments. This work was supported by NIH/NIBIB R01-EB025020.  ... 
arXiv:2003.00355v1 fatcat:x5m5elmmsrarlja5cszkhgwoly

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

Olivier B. Poirion, Zheng Jing, Kumardeep Chaudhary, Sijia Huang, Lana X. Garmire
2021 Genome Medicine  
We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data.  ...  AbstractMulti-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally.  ...  Conclusions DeepProg is a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data.  ... 
doi:10.1186/s13073-021-00930-x pmid:34261540 pmcid:PMC8281595 fatcat:eoxb4nhcqfcjtl73omqsarzmqi

Deep-Time Phylogenetic Clustering of Extinctions in an Evolutionarily Dynamic Clade (Early Jurassic Ammonites)

Clotilde Hardy, Emmanuel Fara, Rémi Laffont, Jean-Louis Dommergues, Christian Meister, Pascal Neige, Vincent Laudet
2012 PLoS ONE  
Here we address this issue by (i) reviewing the approaches used to quantify the phylogenetic selectivity of extinctions and extinction risks; (ii) investigating with a high-resolution dataset whether extinctions  ...  and survivals were phylogenetically clustered among early Pliensbachian (Early Jurassic) ammonites; (iii) exploring the phylogenetic and temporal maintenance of this signal.  ...  This work is part of the project ''PhyloDiv'' and a contribution by the team ''BioME'' of the laboratory Biogéosciences, Dijon (France).  ... 
doi:10.1371/journal.pone.0037977 pmid:22662258 pmcid:PMC3360673 fatcat:7inyge5ywrbsfbthtf7epir5dq

Integration of genomic data analysis for demonstrating potential targets in the subgroup populations of squamous cell lung cancer patients

Yongcui Wang, Weiling Zhao, Xiaobo Zhou
2014 OncoTarget  
a guide to targeted agents that worth to be evaluated in clinical trials for SCC patients with poor survival.  ...  Therefore, development of novel therapeutic approaches is urgently needed. Here, we developed an integrative approach, called DLSA, to integrate genomic, epigenomic and transcriptomic data.  ...  The first step was to learn the survival signatures by associating a deep learning network with patients' survival.  ... 
doi:10.18632/oncotarget.10072 fatcat:6mrtzzsjzvbwjevzu4prjb43vu
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