4 research outputs found
Siamese Labels Auxiliary Network(SiLaNet)
Auxiliary information attracts more and more attention in the area of machine
learning. Attempts so far to include such auxiliary information in
state-of-the-art learning process have often been based on simply appending
these auxiliary features to the data level or feature level. In this paper, we
intend to propose a novel training method with new options and architectures.
Siamese labels, which were used in the training phase as auxiliary modules.
While in the testing phase, the auxiliary module should be removed. Siamese
label module makes it easier to train and improves the performance in testing
process. In general, the main contributions can be summarized as, 1) Siamese
Labels are firstly proposed as auxiliary information to improve the learning
efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary
Network (SilaNet), which is to assist the training of the model; 3) Siamese
Labels Auxiliary Network is applied to compress the model parameters by 50% and
ensure the high accuracy at the same time. For the purpose of comparison, we
tested the network on CIFAR-10 and CIFAR100 using some common models. The
proposed SilaNet performs excellent efficiency both on the accuracy and
robustness
SLC26A4 correlates with homologous recombination deficiency and patient prognosis in prostate cancer
Abstract Background Homologous recombination deficiency (HRD) is closely associated with patient prognosis and treatment options in prostate cancer (PCa). However, there is a lack of quantitative indicators related to HRD to predict the prognosis of PCa accurately. Methods We screened HRD-related genes based on the HRD scores and constructed an HRD cluster system to explore different clinicopathological, genomic, and immunogenomic patterns among the clusters. A risk signature, HRDscore, was established and evaluated by multivariate Cox regression analysis. We noticed that SLC26A4, a model gene, demonstrated unique potential to predict prognosis and HRD in PCa. Multi-omics analysis was conducted to explore its role in PCa, and the results were validated by qRT-PCR and immunohistochemistry. Results Three HRD clusters were identified with significant differences in patient prognosis, clinicopathological characteristics, biological pathways, immune infiltration characteristics, and regulation of immunomodulators. Further analyses revealed that the constructed HRDscore system was an independent prognostic factor of PCa patients with good stability. Finally, we identified a single gene, SLC26A4, which significantly correlated with prognosis in three independent cohorts. Importantly, SLC26A4 was confirmed to distinguish PCa (AUC for mRNA 0.845; AUC for immunohistochemistry score 0.769) and HRD (AUC for mRNA 0.911; AUC for immunohistochemistry score 0.689) at both RNA and protein levels in our cohort. Conclusion This study introduces HRDscore to quantify the HRD pattern of individual PCa patients. Meanwhile, SLC26A4 is a novel biomarker and can reasonably predict the prognosis and HRD in PCa