C. David Page

Human pluripotent stem cell-derived neural constructs for predicting neural toxicity

Significance Stem cell biology, tissue engineering, bioinformatics, and machine learning were combined to implement an in vitro human cellular model for developmental neurotoxicity screening. Human pluripotent stem cell-derived neural tissue constructs with vascular networks and microglia were produced with high sample uniformity by combining precursor cells on synthetic hydrogels using standard culture techniques. Machine learning was used to build a predictive model from changes in global gene expression for neural constructs exposed to 60 toxic and nontoxic training chemicals. The model correctly classified 9 of 10 additional chemicals in a blinded trial. This combined strategy demonstrates the value of human cell-based assays for predictive toxicology and should be useful for both drug and chemical safety assessment. Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single hold-out set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial.

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

PURPOSE To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. MATERIALS AND METHODS The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P = .002), sensitivity (90.0% vs 85.3%, P < .001), and specificity (93.0% vs 88.1%, P < .001). CONCLUSION On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation.

Erratum: area under the precision-recall curve: point estimates and confidence intervals

The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. Similar to the receiver operating characteristic curve, the PR curve has its own unique properties that make estimating its enclosed area challenging. Besides a point estimate of the area, an interval estimate is often required to express magnitude and uncertainty. In this paper we perform a computational analysis of common AUCPR estimators and their confidence intervals. We find both satisfactory estimates and invalid procedures and we recommend two simple intervals that are robust to a variety of assumptions.

Childhood asthma clusters and response to therapy in clinical trials

BACKGROUND Childhood asthma clusters, or subclasses, have been developed by computational methods without evaluation of clinical utility. OBJECTIVE To replicate and determine whether childhood asthma clusters previously identified computationally in the Severe Asthma Research Program (SARP) are associated with treatment responses in Childhood Asthma Research and Education (CARE) Network clinical trials. METHODS A cluster assignment model was determined by using SARP participant data. A total of 611 participants 6 to 18 years old from 3 CARE trials were assigned to SARP pediatric clusters. Primary and secondary outcomes were analyzed by cluster in each trial. RESULTS CARE participants were assigned to SARP clusters with high accuracy. Baseline characteristics were similar between SARP and CARE children of the same cluster. Treatment response in CARE trials was generally similar across clusters. However, with the caveat of a smaller sample size, children in the early-onset/severe-lung function cluster had best response with fluticasone/salmeterol (64% vs 23% 2.5× fluticasone and 13% fluticasone/montelukast in the Best ADd-on Therapy Giving Effective Responses trial; P = .011) and children in the early-onset/comorbidity cluster had the least clinical efficacy to treatments (eg, -0.076% change in FEV1 in the Characterizing Response to Leukotriene Receptor Antagonist and Inhaled Corticosteroid trial). CONCLUSIONS In this study, we replicated SARP pediatric asthma clusters by using a separate, large clinical trials network. Early-onset/severe-lung function and early-onset/comorbidity clusters were associated with differential and limited response to therapy, respectively. Further prospective study of therapeutic response by cluster could provide new insights into childhood asthma treatment.

Randomised restarted search in ILP

Recent statistical performance studies of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a “heavy tail”), restarts can reduce the search cost expectation. We report on an empirical study of randomised restarted search in ILP. Our experiments conducted on a high-performance distributed computing platform provide an extensive statistical performance sample of five search algorithms operating on two principally different classes of ILP problems, one represented by an artificially generated graph problem and the other by three traditional classification benchmarks (mutagenicity, carcinogenicity, finite element mesh design). The sample allows us to (1) estimate the conditional expected value of the search cost (measured by the total number of clauses explored) given the minimum clause score required and a “cutoff” value (the number of clauses examined before the search is restarted), (2) estimate the conditional expected clause score given the cutoff value and the invested search cost, and (3) compare the performance of randomised restarted search strategies to a deterministic non-restarted search. Our findings indicate striking similarities across the five search algorithms and the four domains, in terms of the basic trends of both the statistics (1) and (2). Also, we observe that the cutoff value is critical for the performance of the search algorithm, and using its optimal value in a randomised restarted search may decrease the mean search cost (by several orders of magnitude) or increase the mean achieved score significantly with respect to that obtained with a deterministic non-restarted search.

A human pluripotent stem cell platform for assessing developmental neural toxicity screening

A lack of affordable and effective testing and screening procedures mean surprisingly little is known about the health hazards of many of the tens of thousands of chemicals in use in the world today. The recent rise in the number of children affected by neurological disorders such as autism has stirred valuable debate about the role chemicals play in our daily life, highlighting the need for improved methods of assessing chemicals for developmental neural toxicity.Current methods of testing chemicals for developmental neural toxicity include animal testing with rats or mice and in vitro testing using cultured primary cells or cell lines. Here, we review the current state of neural toxicity screening, analyze the limitations of these methods and, under the National Institutes of Healths new Microphysiological Systems initiative, describe a human pluripotent stem cell-based platform for developmental neural toxicity screens.

Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming

Determining the underlying regulatory mechanism of genetic networks is one of the central challenges of computational biology. Numerous methods have been developed and applied to the important but complex task of reverse engineering regulatory networks from high-throughput gene expression data. However, many challenges remain. In this paper, we are interested in learning rules that will reveal the causal genes for the expression variation from various relational data sources in addition to gene expression data. Following our previous work where we showed that time series gene expression data could potentially uncover causal effects, we describe an application of an inductive logic programming (ILP) system, to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism. Specifically, we learn rules for predicting gene expression levels at the next time step based on the available relational data and then generalize the learned theory to visualize a pruned network of important interactions. We evaluate and present experimental results on microarray experiments from Gasch et alon Saccharomyces cerevisiae.

Cone structure in subjects with known genetic relative risk for AMD.

Purpose Utilize high-resolution imaging to examine retinal anatomy in patients with known genetic relative risk (RR) for developing age-related macular degeneration (AMD). Methods Forty asymptomatic subjects were recruited (9 men, 31 women; age range, 51 to 69 years; mean age, 61.4 years). Comprehensive eye examination, fundus photography, and high-resolution retinal imaging using spectral domain optical coherence tomography and adaptive optics were performed on each patient. Genetic RR scores were developed using an age-independent algorithm. Adaptive optics scanning light ophthalmoscope images were acquired in the macula extending to 10 degrees temporal and superior from fixation and were used to calculate cone density in up to 35 locations for each subject. Results Relative risk was not significantly predictive of fundus grade (p = 0.98). Only patients with a high RR displayed drusen on Cirrus or Bioptigen OCT. Compared to an eye with a grade of 0, an eye with a fundus grade equal to or greater than 1 had a 12% decrease in density (p < 0.0001) and a 5% increase in spacing (p = 0.0014). No association between genetic RR and either cone density (p = 0.435) or spacing (p = 0.538) was found. Three distinct adaptive optics scanning light ophthalmoscope phenotypical variations of photoreceptor appearance were noted in patients with grade 1 to 3 fundi. These included variable reflectivity of photoreceptors, decreased waveguiding, and altered photoreceptor mosaic overlying drusen. Conclusions Our data demonstrate the potential of multimodal assessment in the understanding of early anatomical changes associated with AMD. Adaptive optics scanning light ophthalmoscope imaging reveals a decrease in photoreceptor density and increased spacing in patients with grade 1 to 3 fundi, as well as a spectrum of photoreceptor changes, ranging from variability in reflectivity to decreased density. Future longitudinal studies are needed in genetically characterized subjects to assess the significance of these findings with respect to the development and progression of AMD.

Predicting atrial fibrillation and flutter using Electronic Health Records

Electronic Health Records (EHR) contain large amounts of useful information that could potentially be used for building models for predicting onset of diseases. In this study, we have investigated the use of free-text and coded data in Marshfield Clinics EHR, individually and in combination for building machine learning based models to predict the first ever episode of atrial fibrillation and/or atrial flutter (AFF). We trained and evaluated our AFF models on the EHR data across different time intervals (1, 3, 5 and all years) prior to first documented onset of AFF. We applied several machine learning methods, including naïve bayes, support vector machines (SVM), logistic regression and random forests for building AFF prediction models and evaluated these using 10-fold cross-validation approach. On text-based datasets, the best model achieved an F-measure of 60.1%, when applied exclusively to coded data. The combination of textual and coded data achieved comparable performance. The study results attest to the relative merit of utilizing textual data to complement the use of coded data for disease onset prediction modeling.

EXPERIMENTAL DESIGN OF TIME SERIES DATA FOR LEARNING FROM DYNAMIC BAYESIAN NETWORKS

Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) are becoming more widely used as a way to learn various types of networks, including cellular signaling networks, from high-throughput data. Due to the high cost of performing experiments, we are interested in developing an experimental design for time series data generation. Specifically, we are interested in determining properties of time series data that make them more efficient for DBN modeling. We present a theoretical analysis on the ability of DBNs without hidden variables to learn from proteomic time series data. The analysis reveals, among other lessons, that under a reasonable set of assumptions a fixed budget is better spent on collecting many short time series data than on a few long time series data.