A neural network model for survival data. Stat Med 14, 73–82 (1995) 19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 521, 436–444 (2015) 20. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN...
predictive modelneural networkgrowth/no-growth interfaceE. coli 0157:H7An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the...
For the final analyses, we restricted the set of proteins to compare to only those for which the model predicted that perturbing that protein would produce meaningful changes in cell–cell interactions (defined as the model prediction maximum for a condition being no lower than one-fifth of the...
Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data Data Min. Knowl. Disc., 29 (4) (2015), pp. 1033-1069 CrossrefView in ScopusGoogle Scholar [19] E. Biganzoli, P. Boracchi, L. Mariani, E. Marub...
survival_ganSurvivalGAN is a generative model that can handle survival data by addressing the imbalance in the censoring and time horizons, using a dedicated mechanism for approximating time to event/censoring from the input and survival function.--- ...
内容提示: 1 A multi-layer refined network model for the identification of essential proteins Haoyue Wang, Li Pan * , Bo Yang, Junqiang Jiang and Wenbin Li * Abstract—The identification of essential proteins in protein-protein interaction networks (PINs) can help to discover drug targets and ...
Such network can be used to group similar patients and to associate these groups with distinct features [15]. The main challenges here are: (1) building an appropriate linked data network, discovering a semi-structure of the data model [16] and mapping assertions by the applied model for ...
We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with...
However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to ...
This study proposed and evaluated a deep neural network model for automated renal cell carcinoma classification on both surgical resection and biopsy whole-slide images. We chose ResNet-18 architecture as the backbone of our pipeline, which involved a patch-prediction aggregation strategy. Our final ...