1 code implementation • 26 Dec 2023 • Yibo Kong, George P. Tiley, Claudia Solis-Lemus
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.
1 code implementation • 12 Dec 2022 • Reed Nelson, Rosa Aghdam, Claudia Solis-Lemus
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).
2 code implementations • 30 Nov 2022 • Zhaoxing Wu, Claudia Solis-Lemus
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.
1 code implementation • 1 Nov 2022 • Marianne Bjorner, Erin K. Molloy, Colin N. Dewey, Claudia Solis-Lemus
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.
1 code implementation • 23 Sep 2022 • Samuel Ozminkowski, Yuke Wu, Liule Yang, Zhiwen Xu, Luke Selberg, Chunrong Huang, Claudia Solis-Lemus
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.
2 code implementations • 12 Jan 2022 • Xudong Tang, Leonardo Zepeda-Nunez, Shengwen Yang, Zelin Zhao, Claudia Solis-Lemus
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.
1 code implementation • 29 Nov 2021 • Yuren Sun, Tatiana Midori Maeda, Claudia Solis-Lemus, Daniel Pimentel-Alarcon, Zuzana Burivalova
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.
1 code implementation • 15 Dec 2020 • Yunyi Shen, Claudia Solis-Lemus
Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes.
Applications Methodology 62H10, 62P10
1 code implementation • 10 Dec 2020 • Zhaoyi Zhang, Songyang Cheng, Claudia Solis-Lemus
The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture.