H2020Индивидуална стипендия2020–2022

PhyPPL · First use of probabilistic programming for hard problems in statistical phylogenetics

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-05-04 → 2022-08-24
Финансиране от ЕС: 203 852 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Вероятностното програмиране се прилага в статистическата филогенетика, за да се създават по-точни модели на еволюцията, като например при птиците. Това помага за по-доброто разбиране на биоразнообразието и начина, по който видовете се развиват при промени в климата.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

First use of probabilistic programming for hard problems in statistical phylogenetics

Phylogenetics is a scientific field that aims to reconstruct the evolutionary history of species and understand the factors that may have influenced it. By taking into account the evolutionary relationships between species, we can properly evaluate present and future-day biodiversity, especially in the face of challenges such as climate change. However, despite advances in mathematical modeling, the complexity of planetary interactions can make it challenging to develop accurate models. The Marie Curie Action 898120 (PhyPPL) aimed to push the boundaries of what is possible in computational phylogenetics by adapting the method of universal probabilistic programming to the field. Through several stages of development, including proof of concept, implementation of adapted software, and practical application of the software, the ESR was able to develop a novel and previously un-attempted model with under 1,000 lines of code (anagenetic diversification rate shifts). This accomplishment is a significant achievement as comparable implementations in legacy frameworks often require 10x as much code. The results of the study suggest that probabilistic programming is the best way to specify phylogenetic models, and the team's newly developed model provides a better explanation of the diversification history of birds. Specifically, the study found that many small shifts in the evolutionary rates along the branches of the evolutionary tree may explain diversification patterns better than burst-style changes in rates that had been hypothesized before. These findings have implications not only for the study of bird diversification but also for the broader field of evolutionary biology.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Statistical analysis of phylogenetic models is one of the most active areas of research in computational biology today with wide applications in the Theory of Evolution, epidemiology, forensics, etc. Current phylogenetic software packages limit the user to the set of phylogenetic models and inference strategies that have been pre-programmed in the tool. Inference under certain important phylogenetic models is very difficult with the Markov chain Monte-Carlo strategy implemented in current packages for phylogenetic analysis. The new paradigm of probabilistic programming, coming from computational statistics and theoretical computer science, solves the model expression problem and enables the user to implement novel inference methods. We utilize probabilistic programming to automatically generate Sequential Monte Carlo (SMC) inference machinery for MCMC-hard problems in phylogentics. SMC algorithms may be more efficient, provide unbiased solutions, and provide likelihoods estimates for model comparison.The goal of the proposed research is to carry out some of the first applications of probabilistic programming to real-world problems of empirical interest in evolutionary biology. The objectives are (1) to design and implement statistical inference machinery for complex diversification models with variable tree topology and a trait-dependent branching process under probabilistic programming, (2) to do a pilot study on the applicability of this inference machinery by studying the effect of the orogeny of the Andes on Neotropical biodiversity, and (3) contribute to the design and implementation of a novel probabilistic programming language for phylogenetics, TreePPL, by utilizing the insights gained from (1) and (2).We also propose dissemination and communication measures that target scientists and the general public throughout Europe and in particular new and aspiring EU member states.

Оригинален текст от CORDIS (на английски).

Участници

NATURHISTORISKA RIKSMUSEET · StockholmКоординаторШвеция