Publications by authors named "C M Lisle"
Article Synopsis
- Rhabdomyosarcoma (RMS) is a serious type of cancer affecting primarily kids and young adults, and predicting its mutations at diagnosis is challenging, leading researchers to explore convolutional neural networks (CNN) for better classification and outcome prediction.
- Data from 321 RMS patients were used to train CNNs on histologic images, revealing that the models successfully classified high-risk RMS subtypes and identified mutations linked to survival rates.
- The study found that CNNs outperformed traditional methods for predicting event-free and overall survival in RMS patients, suggesting their potential for improving diagnostic and prognostic strategies in future clinical trials.
Introduction: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions occur in ~ 0.3% of all solid tumours but are enriched in some rare tumour types. Tropomyosin receptor kinase (TRK) inhibitors larotrectinib and entrectinib are approved as tumour-agnostic therapies for solid tumours harbouring NTRK fusions.
View Article and Find Full Text PDFBackground: Mouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors.
View Article and Find Full Text PDFPlant breeding programs evaluate varieties in series of field trials across years and locations, referred to as multi-environment trials (METs). These are an essential part of variety evaluation with the key aim of the statistical analysis of these datasets to accurately estimate the variety by environment (VE) effects. It has previously been thought that the number of varieties in common between environments, referred to as "variety connectivity," was a key driver of the reliability of genetic variance parameter estimation and that this in turn affected the reliability of predictions of VE effects.
View Article and Find Full Text PDFPlant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets.
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