{"doi":"10.1111/j.1558-5646.2011.01236.x","title":"VISUALIZING FITNESS LANDSCAPES","abstract":null,"journal":"Evolution","year":2011,"id":589122,"datarank":2.8894261556887564,"base_score":4.709530201312334,"endowment":4.709530201312334,"self_citation_contribution":0.7064295301968502,"citation_network_contribution":2.1829966254919064,"self_endowment_contribution":0.7064295301968502,"citer_contribution":2.1829966254919064,"corpus_percentile":null,"corpus_rank":null,"citation_count":110,"citer_count":85,"citers_with_citation_signal":67,"citers_with_endowment":67,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":353007,"name":"David M. McCandlish","orcid":"0009-0006-1474-0407","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"VISUALIZING FITNESS LANDSCAPES","abstract":"Fitness landscapes are a classical concept for thinking about the relationship between genotype and fitness. However, because the space of genotypes is typically high-dimensional, the structure of fitness landscapes can be difficult to understand and the heuristic approach of thinking about fitness landscapes as low-dimensional, continuous surfaces may be misleading. Here, I present a rigorous method for creating low-dimensional representations of fitness landscapes. The basic idea is to plot the genotypes in a manner that reflects the ease or difficulty of evolving from one genotype to another. Such a layout can be constructed using the eigenvectors of the transition matrix describing the evolution of a population on the fitness landscape when mutation is weak. In addition, the eigendecomposition of this transition matrix provides a new, high-level view of evolution on a fitness landscape. I demonstrate these techniques by visualizing the fitness landscape for selection for the amino acid serine and by visualizing a neutral network derived from the RNA secondary structure genotype-phenotype map.","is_dataset_classified":null,"base_score":4.709530201312334,"endowment":4.709530201312334,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21644947","pmcid":"PMC3668694","openalex_id":"https://openalex.org/W1680571683","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"P50 GM081883","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"P50GM081883-01","title":null}],"total_grants":2,"fwci":2.7778,"citation_percentile":0.90182297,"influential_citations":0,"citation_trend":[{"year":2012,"count":3},{"year":2013,"count":6},{"year":2014,"count":6},{"year":2015,"count":5},{"year":2016,"count":8},{"year":2017,"count":3},{"year":2018,"count":10},{"year":2019,"count":7},{"year":2020,"count":5},{"year":2021,"count":12},{"year":2022,"count":9},{"year":2023,"count":9},{"year":2024,"count":8},{"year":2025,"count":13},{"year":2026,"count":6}],"oa_status":"green","license":"http://doi.wiley.com/10.1002/tdm_license_1.1","oa_locations":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3668694","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3668694","host_type":"repository"},{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1558-5646.2011.01236.x","host_type":"publisher"},{"url":"http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1558-5646.2011.01236.x/fullpdf","host_type":"publisher"},{"url":"https://doi.org/10.1111/j.1558-5646.2011.01236.x","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/21644947","host_type":"repository"},{"url":"https://repository.cshl.edu/id/eprint/34044/","host_type":"repository"},{"url":"http://europepmc.org/articles/PMC3668694","host_type":"repository"}],"fields_of_study":["Evolution and Genetic Dynamics","Evolutionary Game Theory and Cooperation","Gene Regulatory Network Analysis","Codon","Evolution, Molecular","Genetic Fitness","Genetics, Population","Genotype","Models, Genetic","Phenotype","Protein Structure, Secondary","RNA","Selection, Genetic","Serine"],"mesh_terms":["Codon","Genetics, Population","Genotype","Models, Genetic","Phenotype","RNA","Selection, Genetic","Serine","Protein Structure, Secondary","Evolution, Molecular","Genetic Fitness"],"keywords":["Fitness landscape","Neutral network","Biology","Genetic Fitness","Population","Selection (genetic algorithm)","Evolutionary biology","Biological evolution","Computer science","Artificial intelligence","Genetics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-23T13:14:28.620096Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}