2026-10-14T04:08:01Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33919171 2026-09-18T03:47:03Z category_146 category_7 category_13 category_14 category_45 category_734 category_931 category_64 category_132 category_134 portal_81 item_type_3 month_year_09_2026

Additional file 2 of dicast: a machine learning method for accurate structural variant detection from short-read sequencing data Nico Alavi (25069129) M-Hossein Moeinzadeh (8911901) Jakob Hertzberg (25069132) Uirá Souto Melo (5186753) Lion Ward Al Raei (25069135) Paolo Infantino (25069138) Maryam Ghareghani (19834256) Marco Savarese (108997) Stefan Mundlos (42602) Martin Vingron (5636) Space Science Medicine Genetics Molecular Biology Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Infectious Diseases Virology scores sv calls multiple disease cohorts extensive manual curation candidate pathogenic deletions outperforms existing short machine learning method learning method pathogenic variants true positives technology underlying recovering substantially read callers nine samples new multi human diseases high precision context features consensus approaches common cause clinical workflows Additional file 2: Tables S1–S4. Supplementary tables with legends. Table S1: SV detection methods used per sample for ground truth construction. Table S2: per-sample de novo assembly statistics. Table S3: overlap graph statistics for ground truth construction. Table S4: known pathogenic SVs used in the clinical diagnostic evaluation of dicast. 2026-09-16T05:00:00Z Dataset Dataset 10.6084/m9.figshare.33919171.v1 https://figshare.com/articles/dataset/Additional_file_2_of_dicast_a_machine_learning_method_for_accurate_structural_variant_detection_from_short-read_sequencing_data/33919171 CC BY