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oai:figshare.com:article/34048807 2026-10-01T18:00:04Z category_7 category_13 category_19 category_21 category_272 category_64 category_128 category_133 portal_5 item_type_6 month_year_10_2026

<p>Estimation of LDA coefficients using the entire dataset.</p> Tomoaki Ueda (1780057) Kentaro Fukushima (5227799) Sunggi Chi (25157827) Hiroshi Haeno (206672) Goichi Yoshimoto (25157830) Hironori Arai (10964553) Daisuke Ikeda (425740) Motoshi Ichikawa (25157833) Naoto Takahashi (385073) Naoko Hosono (9512304) Takahiro Yamauchi (534724) Takeshi Kondo (600866) Shigeru Kusumoto (785749) Junya Kuroda (81185) Yosuke Minami (464487) Medicine Genetics Pharmacology Biotechnology Environmental Sciences not elsewhere classified Cancer Hematology Plant Biology mutational patterns classified incomplete hematologic recovery improved overall survival aml ); however acute myeloid leukemia 8 %, 52 cri 33 %). type 1 – type 1 (< type 2 framework type 2 (< div >< p differential response patterns >) groups based treatment ngs data azacitidine plus venetoclax cri following aza among 53 first type 1 1 %, treatment response effective treatment ≥ third world cohort tet2 </ response heterogeneity patients treated nras </ line settings line setting line patients generation sequencing frequently observed cytogenetic analyses complete remission clinical variables clinical determinants 53 genes <p>The preprocessed data were converted to a design matrix using dummy coding for all factors, with the response coded as 0 for nonCR and 1 for CR. Let denote the resulting predictor matrix, and let and be the mean vectors of the predictors in the nonCR and CR groups, respectively. The within-class covariance matrices and were computed for each group, and a pooled within-class covariance matrix, , was then formed, where and are the numbers of nonCR and CR cases. The discriminant weight vector was obtained as and prior class probabilities and were set equal to the empirical class proportions. The scalar intercept for the two-group discriminant function was calculated as For each subject with predictor vector , the linear discriminant score was defined as and classification was given by for CR and for nonCR. The elements of thus quantify the contribution of each standardized predictor to shifting a patient towards the CR or nonCR side of the discriminant boundary.</p> <p>(DOCX)</p> 2026-10-01T18:00:01Z Text Journal contribution 10.1371/journal.pone.0357898.s001 https://figshare.com/articles/journal_contribution/_p_Estimation_of_LDA_coefficients_using_the_entire_dataset_p_/34048807 CC BY 4.0