{"doi":"10.1093/bioinformatics/btac400","title":"PrISM: precision for integrative structural models","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation</jats:title>\n                    <jats:p>A single-precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>We develop PrISM (Precision for Integrative Structural Models) to efficiently identify high- and low-precision regions for integrative models.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation</jats:title>\n                    <jats:p>PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper are available at doi:10.5281/zenodo.6241200.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information</jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.</jats:p>\n                  </jats:sec>","journal":"Bioinformatics","year":2022,"id":625953,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":1619067,"name":"Nikhil Kasukurthi","orcid":null,"position":1,"is_corresponding":false},{"id":565815,"name":"Shruthi Viswanath","orcid":"0000-0002-9061-8407","position":2,"is_corresponding":false},{"id":1334738,"name":"Varun Ullanat","orcid":"0000-0002-1238-7041","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"PrISM: precision for integrative structural models","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation</jats:title>\n                    <jats:p>A single-precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>We develop PrISM (Precision for Integrative Structural Models) to efficiently identify high- and low-precision regions for integrative models.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation</jats:title>\n                    <jats:p>PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper are available at doi:10.5281/zenodo.6241200.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information</jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35723541","pmcid":null,"openalex_id":"https://openalex.org/W4283162683","authors":[],"funders":[{"funder_name":"Department of Science and Technology SERB","grant_id":"SPG/2020/000475","title":null},{"funder_name":"Department of Atomic Energy (DAE) TIFR","grant_id":"RTI 4006","title":null},{"funder_name":"Government of India","grant_id":"","title":null}],"total_grants":3,"fwci":1.1719,"citation_percentile":0.75683453,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2023,"count":3},{"year":2024,"count":2},{"year":2025,"count":4},{"year":2026,"count":1}],"oa_status":"closed","license":"https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model","oa_locations":[{"url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac400/44275910/btac400.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/38/15/3837/49884069/btac400.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bioinformatics/btac400","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35723541","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/07/07/2021.06.22.449385.full.pdf","host_type":"Unpaywall"}],"fields_of_study":["Tensor decomposition and applications","Protein Structure and Dynamics","Bioinformatics and Genomic Networks"],"mesh_terms":["Models, Structural","Software","Benchmarking"],"keywords":["Python (programming language)","Prism","Computer science","Benchmark (surveying)","MIT License","Data mining","License","Algorithm","Software","Programming language","Geology","Optics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T11:40:50.792220Z","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":[]}