Random Forest Model Prediction of Compound Oral Exposure in the Mouse.
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ABSTRACT:
SUBMITTER: Mughal H
PROVIDER: S-EPMC7887840 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. RESULTS:Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including 'CC', 'GG','AG', 'CCCG' and 'CGGC' were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. CONCLUSION:We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.
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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. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.
2012-05-09 | E-GEOD-37858 | biostudies-arrayexpress
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.
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Project description:tRNA fragments (tRFs) are a novel class of small RNAs comparable to the size and function of miRNAs. We and others have shown that tRFs are generally Dicer independent, can be found in abundance in the miRNA effector protein Ago, and can repress expression of specific genes that have complementarity to their 5’ seed-sequences. Given that this greatly expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, using Gene Ontology analysis, we provide evidence that tRF-3009a targets may be involved in neural development. These improvements to tRF target prediction further our understanding of tRF function broadly across species and tRF types, and provide avenues for testing novel roles for tRFs in biology.
2022-05-16 | GSE189510 | GEO
Project description:Cardiovascular disease (CVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random Forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC?=?0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China.
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Project description:Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Random Forests (HARF) that assigns weights to the trees based on the category of the sample. We treat heterogeneity as a latent class allocation problem and present a covariate free class allocation approach based on the distribution of leaf nodes of the model ensemble. Applications on CCLE and GDSC databases show that HARF outperforms traditional Random Forest when the average drug responses of cancer types are different.
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Project description:ObjectiveAlzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).MethodsWe evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software.ResultsOur RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours).ConclusionOur RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
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Project description:Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and residue wise features were extracted for each layer. The method achieves 95.59 % of accuracy using Random Forest with 10 fold cross validation. Prediction of mannose binding site analysis will be quite useful in drug design.
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