Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Exploring The Impact Of Pretrained Bidirectional Language Models On Protein Secondary Structure Prediction, Dillon G. Daudert
Protein secondary structure prediction (PSSP) involves determining the local conformations of the peptide backbone in a folded protein, and is often the first step in resolving a protein's global folded structure. Accurate structure prediction has important implications for understanding protein function and de novo protein design, with progress in recent years being driven by the application of deep learning methods such as convolutional and recurrent neural networks. Language models pretrained on large text corpora have been shown to learn useful representations for feature extraction and transfer learning across problem domains in natural language processing, most notably in instances where ...
Hybrid Model - Statistical Features And Deep Neural Network For Brain Tumor Classification In Mri Images, Mustafa Rashid Ismael
A brain tumor is the most common disease that affects the central nervous system (CNS), the brain, and spinal cord. It can be diagnosed using the safest and most reliable imaging modality, the Magnetic Resonance Imaging (MRI), by radiologists who may use the assistance of computer-aided diagnosis (CAD) tools. Automated diagnosis is sought because it is essential to overcome the drawbacks of the manual diagnosis, such as time and the stress of viewing MRI images for long hours, and the human error potential. Image analysis and machine learning algorithms are tools that can be used to build an intelligent CAD ...