most citedProtocol for Executing and Benchmarking Eight Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis

36 citations · 37 across the 5 of their papers we have counts for

collaborators

6 papers

q-bio.QM2022★ 1 cited

Predicting Survival of Tongue Cancer Patients by Machine Learning Models

Angelos Vasilopoulos, Nan Miles Xi

Tongue cancer is a common oral cavity malignancy that originates in the mouth and throat. Much effort has been invested in improving its diagnosis, treatment, and management. Surgi…

q-bio.QM2022

Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data

Nan Miles Xi, Angelos Vasilopoulos

The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been deve…

stat.AP2022

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay

Jaela Foster-Burns, Nan Miles Xi

Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of drugs and may cause sudden cardiac death. A machine learning model that can accurately identify drug TdP…

cs.LG2022

Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data

Nan Miles Xi, Lin Wang, Chuanjia Yang

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary tre…

q-bio.QM2022

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay

Nan Miles Xi, Dalong Patrick Huang

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug…

q-bio.GN2021★ 36 cited

Protocol for Executing and Benchmarking Eight Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis

Nan Miles Xi, Jingyi Jessica Li

The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational methods have been developed for detecting doublets from scRNA-s…