Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models
Healthcare systems worldwide generate vast amounts of data from many different sources.
Although of high complexity for a human being, it is essential to determine the patterns and …
Although of high complexity for a human being, it is essential to determine the patterns and …
Interpretable machine learning methods for predictions in systems biology from omics data
Machine learning has become a powerful tool for systems biologists, from diagnosing
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …
Predicting protein–peptide binding residues via interpretable deep learning
Identifying the protein–peptide binding residues is fundamentally important to understand
the mechanisms of protein functions and explore drug discovery. Although several …
the mechanisms of protein functions and explore drug discovery. Although several …
A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: From multi-source data integration to knowledge sharing strategies
Data-driven classification models have gained increasing popularity for fault detection and
diagnosis (FDD) tasks considering their advantages in implementation flexibility and …
diagnosis (FDD) tasks considering their advantages in implementation flexibility and …
Prediction of student academic performance using a hybrid 2D CNN model
Opportunities to apply data mining techniques to analyze educational data and improve
learning are increasing. A multitude of data are being produced by institutional technology, e …
learning are increasing. A multitude of data are being produced by institutional technology, e …
A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection
Great advances in deep learning have provided effective solutions for prediction tasks in the
biomedical field. However, accurate prognosis prediction using cancer genomics data …
biomedical field. However, accurate prognosis prediction using cancer genomics data …
Transfer learning with deep tabular models
Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …
tabular models, often bridging the gap between gradient boosted decision trees and neural …
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to
provide an insight into a number of cellular phenotypes from the genomic level and reveal …
provide an insight into a number of cellular phenotypes from the genomic level and reveal …
Transformer for gene expression modeling (T-GEM): an interpretable deep learning model for gene expression-based phenotype predictions
Simple Summary Cancer is the second leading cause of death worldwide. Predicting
phenotype and understanding makers that define the phenotype are important tasks. We …
phenotype and understanding makers that define the phenotype are important tasks. We …
DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning
Background A limitation of traditional differential expression analysis on small datasets
involves the possibility of false positives and false negatives due to sample variation …
involves the possibility of false positives and false negatives due to sample variation …