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Full-Text Articles in Medicine and Health Sciences
Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle
Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle
Theses and Dissertations
Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of …
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney
Theses and Dissertations
Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …