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Found 9 papers, 9 papers with code
Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding
The main reason for the delay in adoption of unsupervised learning in phylogenetics is the lack of a meaningful, yet simple, way of embedding phylogenetic trees into a vector space.
MiNAA: Microbiome Network Alignment Algorithm
Recent implementations of network alignment methods such as NETAL and L-GRAAL also include measures of biological similarity, yet these methods are restricted to one specific type of biological similarity (e. g. sequence similarity in L-GRAAL).
Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants
We illustrate the accuracy and speed of our new method on a variety of simulated scenarios as well as in the estimation of a phylogenetic network for the genus Canis.
Detectability of Varied Hybridization Scenarios using Genome-Scale Hybrid Detection Methods
TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and Patterson's D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events.
BioKlustering: a web app for semi-supervised learning of maximally imbalanced genomic data
We introduce BioKlustering, a user-friendly open-source and publicly available web app for unsupervised and semi-supervised learning specialized for cases when sequence alignment and/or experimental phenotyping of all classes are not possible.
Novel Symmetry-preserving Neural Network Model for Phylogenetic Inference
Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life.
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural Networks
Using soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN) created with transfer learning, we investigate i) the minimum viable training data set size for accurate prediction of call types ('sonotypes'), and ii) the extent to which data augmentation can overcome the issue of small training data sets.
Bayesian Chain Graph LASSO Models to Learn Sparse Microbial Networks with Predictors
Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes.
Applications Methodology 62H10, 62P10
Towards a robust out-of-the-box neural network model for genomic data
The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture.
