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Found 12 papers, 2 papers with code
Inferring multiple consensus trees and supertrees using clustering: a review
Consensus trees and supertrees have been widely used in evolutionary studies to combine phylogenetic information contained in individual gene trees.
Etude de classification des bacteriophages
Phages are one of the most present groups of organisms in the biosphere.
SimPlot++: a Python application for representing sequence similarity and detecting recombination
Motivation: Accurate detection of sequence similarity and homologous recombination are essential parts of many evolutionary analyses.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance
We describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of gene phylogenies.
Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing
Results: In our experiments, we compare the proposed sgRNA-DNA sequence encoding applied in a deep learning prediction framework with state-of-the-art encoding and prediction methods.
Improving cluster recovery with feature rescaling factors
The data preprocessing stage is crucial in clustering.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking
An ensemble method is a ML method that combines multiple hypotheses to form a single hypothesis used for prediction.
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
We propose a general framework for policy representation for reinforcement learning tasks.
VecHGrad for Solving Accurately Complex Tensor Decomposition
Our experiments on five real-world data sets with the state-of-the-art deep learning gradient optimization models show that VecHGrad is capable of converging considerably faster because of its superior theoretical convergence rate per step.
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation
This allows the cluster merging process to start from this partition rather than from a trivial partition composed solely of singletons.
