Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan
{"title":"跨物种知识转移和转化生物医学的计算策略","authors":"Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan","doi":"arxiv-2408.08503","DOIUrl":null,"url":null,"abstract":"Research organisms provide invaluable insights into human biology and\ndiseases, serving as essential tools for functional experiments, disease\nmodeling, and drug testing. However, evolutionary divergence between humans and\nresearch organisms hinders effective knowledge transfer across species. Here,\nwe review state-of-the-art methods for computationally transferring knowledge\nacross species, primarily focusing on methods that utilize transcriptome data\nand/or molecular networks. We introduce the term \"agnology\" to describe the\nfunctional equivalence of molecular components regardless of evolutionary\norigin, as this concept is becoming pervasive in integrative data-driven models\nwhere the role of evolutionary origin can become unclear. Our review addresses\nfour key areas of information and knowledge transfer across species: (1)\ntransferring disease and gene annotation knowledge, (2) identifying agnologous\nmolecular components, (3) inferring equivalent perturbed genes or gene sets,\nand (4) identifying agnologous cell types. We conclude with an outlook on\nfuture directions and several key challenges that remain in cross-species\nknowledge transfer.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational strategies for cross-species knowledge transfer and translational biomedicine\",\"authors\":\"Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan\",\"doi\":\"arxiv-2408.08503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research organisms provide invaluable insights into human biology and\\ndiseases, serving as essential tools for functional experiments, disease\\nmodeling, and drug testing. However, evolutionary divergence between humans and\\nresearch organisms hinders effective knowledge transfer across species. Here,\\nwe review state-of-the-art methods for computationally transferring knowledge\\nacross species, primarily focusing on methods that utilize transcriptome data\\nand/or molecular networks. We introduce the term \\\"agnology\\\" to describe the\\nfunctional equivalence of molecular components regardless of evolutionary\\norigin, as this concept is becoming pervasive in integrative data-driven models\\nwhere the role of evolutionary origin can become unclear. Our review addresses\\nfour key areas of information and knowledge transfer across species: (1)\\ntransferring disease and gene annotation knowledge, (2) identifying agnologous\\nmolecular components, (3) inferring equivalent perturbed genes or gene sets,\\nand (4) identifying agnologous cell types. We conclude with an outlook on\\nfuture directions and several key challenges that remain in cross-species\\nknowledge transfer.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.08503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.08503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational strategies for cross-species knowledge transfer and translational biomedicine
Research organisms provide invaluable insights into human biology and
diseases, serving as essential tools for functional experiments, disease
modeling, and drug testing. However, evolutionary divergence between humans and
research organisms hinders effective knowledge transfer across species. Here,
we review state-of-the-art methods for computationally transferring knowledge
across species, primarily focusing on methods that utilize transcriptome data
and/or molecular networks. We introduce the term "agnology" to describe the
functional equivalence of molecular components regardless of evolutionary
origin, as this concept is becoming pervasive in integrative data-driven models
where the role of evolutionary origin can become unclear. Our review addresses
four key areas of information and knowledge transfer across species: (1)
transferring disease and gene annotation knowledge, (2) identifying agnologous
molecular components, (3) inferring equivalent perturbed genes or gene sets,
and (4) identifying agnologous cell types. We conclude with an outlook on
future directions and several key challenges that remain in cross-species
knowledge transfer.