We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment inter...
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We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment interactions uses genotype and covariate data on affected relative pairs to find evidence for linkage heterogeneity across covariate-defined subgroups. We adapted a likelihood-ratio based test of linkage parameterized with relative risks to a recursivepartitioning framework, including a cross-validation based deviance measurement for choosing optimal tree size and a bootstrap sampling procedure for choosing robust tree structure. ALDX2 category 5 individuals were considered affected, categories 1 and 3 unaffected, and all others unknown. We sampled non-overlapping affected relative pairs from each family;therefore, we used 144 affected pairs in the RP model. Twenty pair-level covariates were defined from smoking status, maximum drinks, ethnicity, sex, and age at onset. Using the all-pairs score in GENEHUNTER, the nonparametric linkage tests showed no regions with suggestive linkage evidence. However, using the RP model, several suggestive regions were found on chromosomes 2, 4, 6, 14, and 20, with detection of associated covariates such as sex and age at onset.
Identification of important structural features for histone deacetylase 8 (HDAC8) inhibitions is very challenging. Design of selective HDAC8 inhibitor may lead to accelerate anticancer drug discovery efforts. Our labo...
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Identification of important structural features for histone deacetylase 8 (HDAC8) inhibitions is very challenging. Design of selective HDAC8 inhibitor may lead to accelerate anticancer drug discovery efforts. Our laboratory has continuously trying to find new leads against HDAC8. In this current study, a statistically significant, robust recursivepartitioning (RP) model was constructed on 588 diverse compounds to develop a decision tree after performing the Bayesian classification study on the same dataset. The statistical quality of the developed RP model was validated externally on 264 compounds like the earlier reported Bayesian classification modeling. The results of this model were compared with previously developed Naive Bayes classifier. In a nutshell, this study is an attempt of acquiring knowledge about the structural features required for selective HDAC8 inhibitors and may help to design new molecules in future. (C) 2019 Elsevier B.V. All rights reserved.
Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In thi...
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Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In this communication, a diverse set comprising large number of compounds are subjected to recursivepartitioning (RP) analysis to develop decision trees to discriminate compounds into HDAC8 inhibitors (active) and non-inhibitors (inactive). Acquiring knowledge about the essential structural and physicochemical parameters can be useful in designing potential and selective HDAC8 inhibitors. Moreover, this work validates our previous results observed in Bayesian modelling study of this dataset. This comparative learning will surely enrich drug discovery aspects related to HDAC8 inhibitors. Communicated by Ramaswamy H. Sarma
The purpose was to compare logistic regression model (LRM) and recursivepartitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built...
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The purpose was to compare logistic regression model (LRM) and recursivepartitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built in a same training set of 496 patients and validated in a same validation set of 337 patients. model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves (AUC)) and calibration. In the training set, AUC were similar for LRM and RP models (0.77 (95% confidence interval, 0.74-0.80) and 0.75 (95% CI, 0.74-0.79), respectively) while LRM outperformed RP in the validation set (0.78 (95% CI, 0.74-0.82) versus 0.64 (95% CI, 0.60-0.67). LRM model also outperformed RP model in term of calibration. In these real datasets, LRM model outperformed RP model. It is therefore more suitable for clinical use.
Background Surgical and systemic treatment modalities for breast cancer (BC) patients with micrometastatic disease in the sentinel lymph node biopsy (SNB) are controversial. The aim of this study was to evaluate decis...
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Background Surgical and systemic treatment modalities for breast cancer (BC) patients with micrometastatic disease in the sentinel lymph node biopsy (SNB) are controversial. The aim of this study was to evaluate decisional factors associated with assignment of adjuvant chemotherapy (CT). Patients and Methods In a retrospective multicentric European study we evaluated cases of primary BC patients who underwent SNB. Logistic regression (LR) and recursivepartitioning analyses (RPA) were performed to determine factors associated with CT. Results Of the 172 patients with micrometastatic disease, 39.5% received adjuvant CT. In the group treated with CT, patients tended to be younger (P?=?0.001), with higher grade (P?=?0.001) and HER2 positive tumors (P?=?0.006) compared to patients without CT. In multivariate LR, age (P?=?0.0027), high grading (P?=?0.01) HER2 positivity (P?=?0.03), and positive non-SN status (P?=?0.03) were significantly associated with CT. RPA demonstrated that tumor grade, and not the non-SN status, was the first split in the partition tree followed by HER2 status, and non-SN status influencing the probability for CT administration. Conclusion High tumor grade is the main decisional factor followed by HER2 positivity and then by the positive non-SN status for CT in micrometastatic disease in the SN. J. Surg. Oncol. 2012;106:703707. (c) 2012 Wiley Periodicals, Inc.
Focusing on chromosome 1, a recursivepartitioning linkage algorithm (RP) was applied to perform linkage analysis on the rheumatoid arthritis NARAC data, incorporating covariates such as HLA-DRB1 genotype, age at onse...
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Focusing on chromosome 1, a recursivepartitioning linkage algorithm (RP) was applied to perform linkage analysis on the rheumatoid arthritis NARAC data, incorporating covariates such as HLA-DRB1 genotype, age at onset, severity, anti-cyclic citrullinated peptide (anti-CCP), and life time smoking. All 617 affected sib pairs from the ascertained families were used, and an RP linkage model was used to identify linkage possibly influenced by covariates. This algorithm includes a likelihood ratio (LR)-based splitting rule, a pruning algorithm to identify optimal tree size, and a bootstrap method for final tree *** strength of the linkage signals was evaluated by empirical p-values, obtained by simulating marker data under null hypothesis of no linkage. Two suggestive linkage regions on chromosome 1 were detected by the RP linkage model, with identified associated covariates HLA-DRB1 genotype and age at onset. These results suggest possible gene x gene and gene x environment interactions at chromosome 1 loci and provide directions for further gene mapping.
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