ArticleBMC genomics2020
Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- PhyloTune: An efficient method to accelerate phylogenetic updates using a pretrained DNA language model.Nature communications · 2025Article
- Machine learning solutions for integrating partially overlapping genetic datasets and modelling host-endophyte effects in ryegrass (Frontiers in plant science · 2025Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- AInternational journal of molecular sciences · 2023Article
- PhyloMissForest: a random forest framework to construct phylogenetic trees with missing data.BMC genomics · 2022Article
- Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.Genes · 2022Article
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model.Bioinformatics advances · 2022Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Novel metric for hyperbolic phylogenetic tree embeddings.Biology methods & protocols · 2021Article
- Detecting introgression from phylogenetic invariant site patterns using machine learning.Applications in plant sciencesArticle
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2 authors.
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Abstract
backgroundWith the rapid growth rate of newly sequenced genomes, species tree inference from genes sampled throughout the whole genome has become a basic task in comparative and evolutionary biology. However, substantial challenges remain in leveraging these large scale molecular data. One of the foremost challenges is to develop efficient methods that can handle missing data. Popular distance-based methods, such as NJ (neighbor joining) and UPGMA (unweighted pair group method with arithmetic mean) require complete distance matrices without any missing data.
resultsWe introduce two highly accurate machine learning based distance imputation techniques. These methods are based on matrix factorization and autoencoder based deep learning architectures. We evaluated these two methods on a collection of simulated and biological datasets. Experimental results suggest that our proposed methods match or improve upon the best alternate distance imputation techniques. Moreover, these methods are scalable to large datasets with hundreds of taxa, and can handle a substantial amount of missing data.
conclusionsThis study shows, for the first time, the power and feasibility of applying deep learning techniques for imputing distance matrices. Thus, this study advances the state-of-the-art in phylogenetic tree construction in the presence of missing data. The proposed methods are available in open source form at https://github.com/Ananya-Bhattacharjee/ImputeDistances .
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