Biological foundation models learn evolutionary rules
Claim that biological foundation models, trained on massive and evolutionarily diverse genome datasets, uncover emergent rules governing molecular evolution.
Evidence Quote
“By learning on such a massive scale, it's believed that the model will uncover an emergent set of rules governing molecular evolution”
Relationship
Connections (6)
Evidence
“Reference on using evolutionary approaches to find organismal models for human biology.”
“Evidence outlining new opportunities at the wild frontier for model organism research, supported by the cited references.”
“Preprint on genome modeling and design using Evo 2 model”
“Study using a language model to simulate extensive evolutionary time”
“Study on scaling models for protein generation and function understanding”
“Study showing protein language models learn phylogenetic relationships when trained on MSAs”
“Inquiry into whether protein language models capture phylogenetic information”
“Reference to the arcadiathemeR R package by Arcadia Science.”
“Reference to the arcadia-pycolor Python package by Arcadia Science.”
“Reference to the textstat Python package.”
“"Evidence detailing NIH support for model organism research, associated with the cited reference "A Look at NIH Support for Model Organisms”
“Evidence summarizing publication trends in research involving model organisms, associated with the cited reference "Publication Trends in Model Organism Research".”
“Evidence summarizing trends and insights on animal models used in preclinical gene therapy product studies, based on the cited reference "A review of animal models utilized in preclinical studies of approved gene therapy products: trends and insights".”
“Evidence summarizing mouse models of human disease from an evolutionary standpoint supported by the citation "Mouse Models of Human Disease: An Evolutionary Perspective".”