Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification.

HIV-1 Reverse Transcriptase Error Rates and Transcriptional Thresholds Based on Single-strand Consensus Sequencing of Target RNA Derived From In Vitro-transcription and HIV-infected Cells.

Nucleotide incorporation and lacZ-based forward mutation assays have been widely used to determine the accuracy of reverse transcriptases (RTs) in RNA-dependent DNA polymerization reactions. However, they involve quite complex and laborious procedures, and cannot provide accurate error rates. Recently, NGS-based methods using barcodes opened the possibility of detecting all errors introduced by the RT, although their widespread use is limited by cost, due to the large size of libraries to be sequenced.