Biology subjects

Rosas-Puchuri, U.

Publications and source records attributed to Rosas-Puchuri, U..

3 recordsLinked to original sources

bioRxiv2025

Sparse learning for scalable phylogenetic network inference

Phylogenetic networks account for signals of hybridization, reticulation, and gene flow, and provide an opportunity to analyse species evolution from a more complex perspective than bifurcating phylogenetic trees. However, even the fastest algorithms for inferring these networks face scalability challenges as the number of species increases. This limitation arises because these methods use as input a large concordance factors (CFs) table, which summarizes the observed CFs of all possible four-species combinations in each row. The size of this table scales with the fourth power of the number of species, creating computational bottlenecks and highlighting the need for more efficient solutions. Sparse learning has been shown to reduce the dimensionality of large-scale datasets while producing results of comparable quality to those obtained using the full dataset. In this study, we adapted two sparse machine learning models--Elastic Net and Ensemble Learning + Elastic Net--to guide the subsample of an optimal number of rows from the CFs table required to accurately predict the overall phylogenetic network pseudolikelihood. Both methods account for the inherent correlation among rows, which arises because rows overlap in species information. We call this method Qsin. In two simulated datasets, Qsin reduced the dataset by approximately half without compromising accuracy. For the Xiphophorus fishes dataset, which contains 10,626 rows in the CFs table, we recovered the same topology as with the full CFs table but using only 763 rows. Using these subsamples also reduced running times by up to 60% without compromising accuracy. These gains are expected to persist as species numbers increase. Qsin contributes to ongoing efforts to make phylogenetic network inference more efficient and opens the door to analyses of more complex evolutionary histories. The source code for Qsin is freely available at: https://github.com/ulises-rosas/qsin.

evolutionary biology↗

bioRxiv2023

Non-linear phylogenetic regression using regularized kernels

O_LIPhylogenetic regression is a type of Generalized Least Squares (GLS) method that incorporates a covariance matrix based on the evolutionary relationships between species (i.e., phylogenetic relationships). While this method has found widespread use in hypothesis testing via comparative phylogenetic methods, such as phylogenetic ANOVA, its ability to account for non-linear relationships has received little attention. C_LIO_LITo address this issue, we utilized GLS in a high-dimensional feature space, employing linear combinations of transformed data to account for non-linearity, a common approach in kernel regression. We analyzed two biological datasets using both Radial Basis Function (RBF) and linear kernel transformations. The first dataset contained morphometric data, while the second dataset comprised discrete trait data and diversification rates as labels. Hyperparameter tuning of the model was achieved through cross-validation rounds in the training set. C_LIO_LIIn the tested biological datasets, regularized kernels reduced the error rate (as measured by RMSE) by around 20% compared to linear-based regression when data did not exhibit linear relationships. In simulated datasets, the error rate decreased almost exponentially with the level of non-linearity. C_LIO_LIThese results show that introducing kernels into phylogenetic regression analysis presents a novel and promising tool for complementing phylogenetic comparative methods. We have integrated this method into Python package named phyloKRR, which is freely available at: https://github.com/Ulises-Rosas/phylokrr. C_LI

evolutionary biology↗

bioRxiv2023

Dissecting Factors Underlying Phylogenetic Uncertainty Using Machine Learning Models

Phylogenetic inference can be influenced by both underlying biological processes and methodological factors. While biological processes can be modeled, these models frequently make the assumption that methodological factors do not significantly influence the outcome of phylogenomic analyses. Depending on their severity, methodological factors can introduce inconsistency and uncertainty into the inference process. Although search protocols have been proposed to mitigate these issues, many solutions tend to treat factors independently or assume a linear relationship among them. In this study, we capitalize on the increasing size of phylogenetic datasets, using them to train machine learning models. This approach transcends the linearity assumption, accommodating complex non-linear relationships among features. We examined two phylogenomic datasets for teleost fishes: a newly generated dataset for protacanthopterygians (salmonids, galaxiids, marine smelts, and allies), and a reanalysis of a dataset for carangarians (flatfishes and allies). Upon testing five supervised machine learning models, we found that all outperformed the linear model (p < 0.05), with the deep neural network showing the best fit for both empirical datasets tested. Feature importance analyses indicated that influential factors were specific to individual datasets. The insights obtained have the potential to significantly enhance decision-making in phylogenetic analyses, assisting, for example, in the choice of suitable DNA sequence models and data transformation methods. This study can serve as a baseline for future endeavors aiming to capture non-linear interactions of features in phylogenomic datasets using machine learning and complement existing tools for phylogenetic analyses.

evolutionary biology↗