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algorithms: single data types, low feature extraction efficiency, and low classification network accuracy. A tumor types prediction model based on deep learning is proposed. The network model uses ...
of these techniques, mainly to enhance automated decision-making. In order to improve the classification precision, our study proposes a convolutional neural networks - based framework with auxiliary information. Firstly ...
classification . In this study, we developed and rigorously assessed an attention- based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51 ...
in predictors that follow some known finite discrete distribution. We then conducted a case study using the framework to predict treatment outcome for tuberculosis patients during their course of treatment ...
was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved ...
of LoNFRS, which is cumulatively quantified as NFRs significance measure (NFRINDEX). NFRINDEX has been quantified for prediction purposes using an convolution neural network (CNN). Additionally, the existence ...
and accurate subtype prediction are critical for treatment . Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic ...
the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification . GraphChrom was developed using a graph neural network which models the complex structure ...
classification accuracy. Researchers in this study used structural MRI data to develop a deep learning framework for combined automatic hippocampus segmentation and AD categorization. Multi-task deep learning ...
, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks . Specifically, we design a convolutional ...
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