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1. Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

   https://doi.org/10.1038/s41540-024-00381-1  

   Balzerani, Francesco; Blasco, Telmo; Pérez-Burillo, Sergio; Valcarcel, Luis V; Hassoun, Soha; Planes, Francisco J (December 2024, npj Systems Biology and Applications)

   Abstract Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition. 

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2. A New Era of Morphological Investigations: Reviewing Methods for Comparative Anatomical Studies

   https://doi.org/10.1093/iob/obad008  

   Ford, K. L.; Albert, J. S.; Summers, A. P.; Hedrick, B. P.; Schachner, E. R.; Jones, A. S.; Evans, K.; Chakrabarty, P. (March 2023, Integrative Organismal Biology)

   Synopsis The increased use of imaging technology in biological research has drastically altered morphological studies in recent decades and allowed for the preservation of important collection specimens alongside detailed visualization of bony and soft-tissue structures. Despite the benefits associated with these newer imaging techniques, there remains a need for more “traditional” methods of morphological examination in many comparative studies. In this paper, we describe the costs and benefits of the various methods of visualizing, examining, and comparing morphological structures. There are significant differences not only in the costs associated with these different methods (monetary, time, equipment, and software), but also in the degree to which specimens are destroyed. We argue not for any one particular method over another in morphological studies, but instead suggest a combination of methods is useful not only for breadth of visualization, but also for the financial and time constraints often imposed on early-career research scientists. 

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3. Sublinear Time Approximation of Text Similarity Matrices

   Ray, Archan; Monath, Nicholas; McCallum, Andrew; Musco, Cameron (February 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a similarity matrix for $$n$$ data points requires $$\Omega(n^2)$$ similarity computations. This quadratic scaling is a significant bottleneck, especially when similarities are computed via expensive functions, e.g., via transformer models. Approximation methods reduce this quadratic complexity, often by using a small subset of exactly computed similarities to approximate the remainder of the complete pairwise similarity matrix. Significant work focuses on the efficient approximation of positive semidefinite (PSD) similarity matrices, which arise e.g., in kernel methods. However, much less is understood about indefinite (non-PSD) similarity matrices, which often arise in NLP. Motivated by the observation that many of these matrices are still somewhat close to PSD, we introduce a generalization of the popular \emph{Nystrom method} to the indefinite setting. Our algorithm can be applied to any similarity matrix and runs in sublinear time in the size of the matrix, producing a rank-$$s$$ approximation with just $O(ns)$ similarity computations. We show that our method, along with a simple variant of CUR decomposition, performs very well in approximating a variety of similarity matrices arising in NLP tasks. We demonstrate high accuracy of the approximated similarity matrices in tasks of document classification, sentence similarity, and cross-document coreference. 

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4. Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints

   Xinmeng Huang, Donghwan Lee (December 2022, Advances in neural information processing systems)

   In modern machine learning, users often have to collaborate to learn distributions that generate the data. Communication can be a significant bottleneck. Prior work has studied homogeneous users—i.e., whose data follow the same discrete distribution—and has provided optimal communication-efficient methods. How- ever, these methods rely heavily on homogeneity, and are less applicable in the common case when users’ discrete distributions are heterogeneous. Here we consider a natural and tractable model of heterogeneity, where users’ discrete distributions only vary sparsely, on a small number of entries. We propose a novel two-stage method named SHIFT: First, the users collaborate by communicating with the server to learn a central distribution; relying on methods from robust statistics. Then, the learned central distribution is fine-tuned to estimate the indi- vidual distributions of users. We show that our method is minimax optimal in our model of heterogeneity and under communication constraints. Further, we provide experimental results using both synthetic data and n-gram frequency estimation in the text domain, which corroborate its efficiency. 

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5. Species Tree Inference Methods Intended to Deal with Incomplete Lineage Sorting Are Robust to the Presence of Paralogs

   https://doi.org/10.1093/sysbio/syab056  

   Yan, Zhi; Smith, Megan L; Du, Peng; Hahn, Matthew W; Nakhleh, Luay (July 2021, Systematic Biology)

   Kubatko, Laura (Ed.)

   Abstract Many recent phylogenetic methods have focused on accurately inferring species trees when there is gene tree discordance due to incomplete lineage sorting (ILS). For almost all of these methods, and for phylogenetic methods in general, the data for each locus are assumed to consist of orthologous, single-copy sequences. Loci that are present in more than a single copy in any of the studied genomes are excluded from the data. These steps greatly reduce the number of loci available for analysis. The question we seek to answer in this study is: what happens if one runs such species tree inference methods on data where paralogy is present, in addition to or without ILS being present? Through simulation studies and analyses of two large biological data sets, we show that running such methods on data with paralogs can still provide accurate results. We use multiple different methods, some of which are based directly on the multispecies coalescent model, and some of which have been proven to be statistically consistent under it. We also treat the paralogous loci in multiple ways: from explicitly denoting them as paralogs, to randomly selecting one copy per species. In all cases, the inferred species trees are as accurate as equivalent analyses using single-copy orthologs. Our results have significant implications for the use of ILS-aware phylogenomic analyses, demonstrating that they do not have to be restricted to single-copy loci. This will greatly increase the amount of data that can be used for phylogenetic inference.[Gene duplication and loss; incomplete lineage sorting; multispecies coalescent; orthology; paralogy.] 

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