Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

Bhattacharjee, Ananya; Bayzid, Md. Shamsuzzoha

doi:10.1101/744789

Cited by 9 publications

(13 citation statements)

References 72 publications

(109 reference statements)

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“…In such case, the distance d ij for

and

is missing. Imputing such missing distances is important to estimate the comprehensive tree [ 35 ]. In this research, we proposed an alternative approach for imputing distances based on the eigenvalue of the indefinite inner product matrix

.…”

Section: Methodsmentioning

confidence: 99%

Novel metric for hyperbolic phylogenetic tree embeddings

Matsumoto

Mimori

Fukunaga

2021

Biology Methods and Protocols

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Advances in experimental technologies such as DNA sequencing have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. Furthermore, we proposed a novel approach based on our metric to integrate multiple trees for analyzing tree nodes or imputing missing distances. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods.

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“…In such case, the distance d ij for

and

.…”

Section: Methodsmentioning

confidence: 99%

Novel metric for hyperbolic phylogenetic tree embeddings

Matsumoto

Mimori

Fukunaga

2021

Biology Methods and Protocols

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“…Given the backbone tree with its associated and query sequences, the model outputs an embedding of the query and reference species which can be used as input to some distance-based phylogenetic placement tools, which then places the query sequences onto the reference tree. Bhattacharjee et al 115 addressed the data imputation problem in the incomplete distance matrix using autoencoders. However, the key limitation of these methods is that trees cannot be reliably embedded into a Euclidean space of low dimensions 116 .…”

Section: Minor Successes Of DLmentioning

confidence: 99%

Current progress and open challenges for applying deep learning across the biosciences

et al. 2022

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Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

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“…Works using machine learning for phylogenetic tree construction already exists. In [1], they introduced an approach to the case where the distance matrix is incomplete. By using deep architectures, they could eliminate the need for a molecular clock assumption, representing a real-world occurrence of the problem.…”

Section: Related Workmentioning

confidence: 99%

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

Lipták

Kiss

2021

Studia UBB Informatica

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With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

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Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

Cited by 9 publications

References 72 publications

Novel metric for hyperbolic phylogenetic tree embeddings

Novel metric for hyperbolic phylogenetic tree embeddings

Current progress and open challenges for applying deep learning across the biosciences

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

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