149 research outputs found
Genomic Biomarkers for Personalized Medicine in Breast Cancer
Breast cancer is the most common malignant disease in Western women. Historically, breast cancer was perceived as a single disease with various clinicalpathological features, and therefore, āone drug fits allā approaches drove the treatment regimens. The advent of genomics studies has led to a new paradigm in which breast cancer is heterogeneous consisting of different diseases from the same organ site. For example, gene expression profiling
analysis revealed that estrogen receptor (ER)-positive and ER negative breast cancer are two distinct diseases with different risk factors, clinical presentations, outcomes, and responses to systemic therapies. Consequently, the new paradigm demands a personalized strategy in cancer medicine, in which the selection of treatment regimens for each cancer patient will largely rely on assessment by predictive biomarkers and study of the anatomical
and pathological features of the cancer
Development of Estrogen Receptor Beta Binding Prediction Model Using Large Sets of Chemicals
We developed an ERĪ² binding prediction model to facilitate identification of chemicals specifically bind ERĪ² or ERĪ± together with our previously developed ERĪ± binding model. Decision Forest was used to train ERĪ² binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERĪ² binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERĪ² binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERĪ² binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERĪ± prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERĪ² or ERĪ±
\u3ci\u3eIn silico\u3c/i\u3e identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of \u3ci\u3eKochia scoparia\u3c/i\u3e
Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis
Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence
Class prediction using āomicsā data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request
Development of a Nicotinic Acetylcholine Receptor nAChR Ī±7 Binding Activity Prediction Model
Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-Ī±7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-Ī±7 is an important component for assessing addictive potential of tobacco constituents. We developed an Ī±7 binding activity prediction model based on a large training data set of 843 chemicals with human Ī±7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat Ī±7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptorāligand binding. A decision forest was used to train the human Ī±7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human Ī±7 binding activity for 5275 tobacco constituents. The human Ī±7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human Ī±7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents
Evaluation of gene expression data generated from expired Affymetrix GeneChipĀ® microarrays using MAQC reference RNA samples
BACKGROUND: The Affymetrix GeneChip(Ā®) system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip(Ā®) microarrays unprocessed before their manufacturerās expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions. RESULTS: Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan(Ā®) data generated by the MAQC project in 2005. CONCLUSIONS: Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturerās expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, stored at -80Ā°C, were stable over a time frame of at least four years
Deep Learning Architectures for Multi-Label Classification of Intelligent Health Risk Prediction
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