Random Forest DRAGON model (local for acidic compounds) for predicting human plasma protein binding (square-root of fraction unbound) for CS1 compounds.
Ontology highlight
ABSTRACT:
SUBMITTER: Domenico Gadaleta
PROVIDER: S-TOXR905 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.
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Project description:ObjectivesTo describe the unbound and total flucloxacillin pharmacokinetics in critically ill patients and to define optimal dosing strategies.Patients and methodsObservational multicentre study including a total of 33 adult ICU patients receiving flucloxacillin, given as intermittent or continuous infusion. Pharmacokinetic sampling was performed on two occasions on two different days. Total and unbound flucloxacillin concentrations were measured and analysed using non-linear mixed-effects modelling. Serum albumin was added as covariate on the maximum binding capacity and endogenous creatinine clearance (CLCR) as covariate for renal function. Monte Carlo simulations were performed to predict the unbound flucloxacillin concentrations for different dosing strategies and different categories of endogenous CLCR.ResultsThe measured unbound concentrations ranged from 0.2 to 110 mg/L and the observed unbound fraction varied between 7.0% and 71.7%. An integral two-compartmental linear pharmacokinetic model based on total and unbound concentrations was developed. A dose of 12 g/24 h was sufficient for 99.9% of the population to achieve a concentration of >2.5 mg/L (100% fT>5×MIC, MIC = 0.5 mg/L).ConclusionsCritically ill patients show higher unbound flucloxacillin fractions and concentrations than previously thought. Consequently, the risk of subtherapeutic exposure is low.
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Project description:The mathematical foundation of quantum mechanics is built on linear algebra, while the application of nonlinear operators can lead to outstanding discoveries under some circumstances, such as the prediction of positron, a direct outcome of the Dirac equation which stems from the square-root of the Klein-Gordon equation. In this article, we propose a model of square-root higher-order Weyl semimetal (SHOWS) by inheriting features from its parent Hamiltonians. It is found that the SHOWS hosts both "Fermi-arc" surface and hinge states that respectively connect the projection of the Weyl points on the side surface and arris. We theoretically construct and experimentally observe the exotic SHOWS state in three-dimensional (3D) stacked electric circuits with honeycomb-kagome hybridizations and double-helix interlayer couplings. Our results open the door for realizing the square-root topology in 3D solid-state platforms.
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Project description:Successive plantings of Prunus species produce suboptimal growth and yield in many California soils due to a poorly understood soilborne disease complex, Prunus replant disease (PRD). We explored the hypothesis that PRD is mediated by microbial taxa in roots of Nemaguard peach, a rootstock for almond and other stone fruits. In a greenhouse bioassay, portions of 10 replant soils were treated with fumigation or pasteurization or left untreated as a control before being planted with peach seedlings. Ten weeks after planting, seedlings were considered PRD-affected if their top fresh weights in the control were significantly reduced, compared to the weights in pasteurization and fumigation treatments; plants with equivalent top weights in all treatments were considered to be non-affected. The roots were washed from the soil, frozen, extracted for total DNA, and used for metabarcoding of rRNA gene amplicons from bacteria, fungi, and oomycetes. High-throughput amplicon sequencing revealed that root microbial community shifts resulted from preplant treatments, and specific taxa were associated with PRD induction among controls. Random forest (RF) analysis discriminated effectively between PRD-affected and non-affected root communities. Among the 30 RF top-ranked amplicon sequence variant (ASV) predictors, 26 were bacteria, two were oomycetes, and two were fungi. Among them, only Streptomyces scabiei, Steroidobacter denitrificans, Streptomyces bobili, and Pythium mamillatum had root abundances ≥5% that were either associated positively (former two ASVs) or negatively (latter two) with PRD. Thus, our findings were consistent with microbial mediation of PRD in roots and suggested taxa that may be involved in the mediation.
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Project description:MotivationHigh-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings.ResultsWe characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of association between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a permutation test to estimate empirical P-values of SNPs. Our method was compared to a univariate test and the permutation test using the Gini and permutation importance. In simulation, the proposed method performed consistently superior to the other methods in identifying of risk SNPs. In a GWAS of age-related macular degeneration, the proposed method confirmed two significant SNPs (at the genome-wide adjusted level of 0.05). Further analysis showed that these two SNPs conformed with a heterogeneity model. Compared with the existing importance measures, the MCC importance measure is more sensitive to complex effects of risk SNPs by utilizing conditional information on different SNPs. The permutation test with the MCC importance measure provides an efficient way to identify candidate SNPs in GWAS and facilitates the understanding of the etiology between genetic variants and complex diseases.Contactheping.zhang@yale.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We here present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=78) were used as input. The model was validated with data from two independent PCa cohorts from ICGC and TCGA. Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason, stage, nodal stage and PSA. In summary, we have developed a PCa classification model which either directly or via expression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.
2019-10-29 | GSE127985 | GEO
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction.
2012-05-10 | GSE37858 | GEO