Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here,...
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsuperv...
Multiplex DNA methylation and glycosylation are ubiquitous in the human body to ensure the normal function and stability of the genome. The methyltransferases and glycosylases rely on varied enzymes with different action mechanism, which still remain...
OBJECTIVE: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that enc...
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from...
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss fu...
It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that comb...
IEEE journal of biomedical and health informatics
Oct 1, 2019
Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell popul...
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absen...
Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope direc...
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