[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127427-en":3,"doc-seo-127427-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127427,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Comparison of machine learning algorithms for species clustering based on DEB traits","Machine learning supports learning algorithms that infer patterns from data and generalize to unseen cases. Clustering, a key unsupervised task, forms groups based on similarity, enabling related organisms to be organized without explicit labels. This paper compares multiple clustering algorithms for species grouping using the Add-My-Pet database and Dynamic Energy Budget (DEB) traits, evaluating how well each method captures trait-based structure and supports more accurate estimation of features for newly encountered organisms.","Comparison of machine learning algorithms for species clustering  \nbased on DEB traits  \nEleni Bampou  \nFebruary 2024  \nAbstract  \nMachine learning is a rapidly developing branch of artificial intelligence comprising a collection of algorithms that are concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Clustering is a form of unsupervised learning in which the chosen algorithm constructs groups or clusters of the given data. Given a measure of similarity, a cluster is characterized by how close together its containing data are. On one hand, many tools for cluster analysis were created, on the other, each has its strengths and weaknesses, due to the complexity of information. In this paper, using the Add-My-Pet database, some clustering algorithms are tested and compared in their ability to group species of organisms by their traits, as given by the Dynamic Energy Budget (DEB) theory.  \n1 Introduction  \nClustering algorithms in machine learning fall within the realm of unsupervised learning, where the model is provided with input (features) but lacks corresponding output labels or targets. In such scenarios, the algorithm leverages past training data to formulate predictions. In the context of this work, the training set is derived from the Add-My-Pet collection [11], a comprehensive dataset pairing organisms with their distinctive traits, providing insights into the intricate metabolic processes of each organism. Utilizing a variety of metrics on those traits, the clustering algorithms embark on the task of generating clusters within the dataset. The primary objective is to categorize similar organisms together, facilitating the subsequent classification of new organisms. The ultimate goal is to enhance predictive accuracy when estimating the traits of these newly encountered organisms, contributing toa more nuanced understanding of their metabolic characteristics.  \n2 Dynamic Energy Budget Theory  \nThe Dynamic Energy Budget (DEB) theory [6] serves as a comprehensive metabolic framework, providing a unified quantitative description of an organism’s metabolic processes in a dynamic environment. DEB theory captures the metabolic dynamics of an individual organism throughout its entire life cycle, covering the start of embryo development to death by aging through a range of life stages. Based on assumptions about the acquisition, storage, and utilization of energy and nutrients, DEB theory complies rigorously with thermodynamic principles. It is not specific to any particular species and establishes connections across different biological levels (cells, organisms, and populations) as dictated by energetic implications. DEB models are meant to apply to all life on earth and allow species comparisons on the basis of (functions of) parameters. DEB offers practical applications for assessing the quality of measurements and handling missing data in unmeasured traits.  \nDifferential equations, derived from a small set of assumptions, describe how state variables of an individual (reserve energy, structural length, cumulative energy investment into maturation and reproduction) change in time through all life stages. Each life stage is characterized by specific metabolic behaviors: embryos do not feed; juveniles feed and allocate to maturity but not to reproduction; adults feed, no longer allocate to maturation, instead allocate energy/mass towards the storage of reproduction buffer, which subsequently is converted to offspring. Each DEB parameter is linked with one underlying process and the combination of parameters covers all aspects of energetics throughout the full life cycle of organisms, include feeding, digestion, storage, maintenance, growth, development, reproduction, aging. Parameter values are individual-specific in the context of DEB theory, but the difference between individuals are typically small e","cbCaisPfvWOP44fc","https://ap.wps.com/l/cbCaisPfvWOP44fc","pdf",709540,1,19,"English","en",105,"# Introduction\n## Unsupervised clustering objective\n# Dynamic Energy Budget Theory\n## Life-cycle metabolic modeling\n## Auxiliary theory and parameter estimation\n# Add-My-Pet (AmP) Database\n## Traits, measured data, and implied traits\n## Data coverage and species comparison","[{\"question\":\"What is the main goal of the paper’s species clustering approach?\",\"answer\":\"To group similar organisms together using clustering algorithms trained on DEB traits from the Add-My-Pet dataset, improving predictive accuracy for estimating traits of new organisms.\"},{\"question\":\"How does DEB theory support the clustering task?\",\"answer\":\"DEB theory provides a unified metabolic framework across life stages and species, defining measurable and inferred traits via model parameters and auxiliary measurable quantities.\"},{\"question\":\"What role does the Add-My-Pet (AmP) database play?\",\"answer\":\"AmP supplies open-access data including measured traits used for parameter estimation and implied traits derived from DEB parameters, enabling cross-species clustering evaluation.\"}]","Comparison of machine learning algorithms for species clustering based on DEB traits | 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is the main goal of the paper’s species clustering approach?","Question",{"text":76,"@type":77},"To group similar organisms together using clustering algorithms trained on DEB traits from the Add-My-Pet dataset, improving predictive accuracy for estimating traits of new organisms.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does DEB theory support the clustering task?",{"text":81,"@type":77},"DEB theory provides a unified metabolic framework across life stages and species, defining measurable and inferred traits via model parameters and auxiliary measurable quantities.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does the Add-My-Pet (AmP) database play?",{"text":85,"@type":77},"AmP supplies open-access data including measured traits used for parameter estimation and implied traits derived from DEB parameters, enabling cross-species clustering 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