ACA BYOL qPCR datasets and model checkpoints
Year: 2026
Type: dataset
Abstract: Associated manuscriptStoichiometric encoding of amplification kinetics enables high-level multiplexing in single-channel TaqMan real-time PCRLouis Kreitmann, Kenny Malpartida-Cardenas, Ye Mao, Zexuan Zhao, San Chun Hin, Anirudhha Hazarika, Ke Xu, Luca Miglietta, Zara Breese, Alison H. Holmes, Karen Brengel-Pesce, Laurent Drazek, and Jesus Rodriguez-Manzano.This repository contains the processed amplification curves (ACs) datasets and trained model checkpoints supporting the analyses reported in the manuscript above.The accompanying analysis and model-training code is available at:https://github.com/lkreitmann-bmx/ACA_BYOL_qPCR---OverviewThe study investigates amplification curve analysis (ACA) as a strategy for increasing the multiplexing capacity of TaqMan real-time PCR (qPCR). It combines:Stoichiometric feature encoding, in which forward primer, reverse primer, and probe concentrations are varied to generate target-specific AC morphologies.Self-supervised representation learning using Bootstrap Your Own Latent (BYOL) on digital qPCR (dqPCR) amplification curves.Conditional domain adversarial training using a transformer-based CDAN (T-CDAN) to improve transfer across experimental domains.Co-amplification analysis to determine whether double- and triple-target amplification events remain distinguishable.The deposit is intended to provide the datasets required to reproduce the main analyses and the trained neural-network checkpoints used in the manuscript. Additional raw instrument files are not included in this deposit and are available from the corresponding author upon reasonable request, subject to institutional and/or commercial data-sharing agreements.
Source: Figshare
Author: Louis Kreitmann
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212436534