Challenges and opportunities for omics-based precision medicine in chronic low back pain.

Purpose: Chronic low back pain (cLBP) is a common health condition worldwide and a leading cause of disability with an estimated lifetime prevalence of 80-90% in industrialized countries. However, we have had limited success in treating cLBP likely due to its non-specific heterogeneous nature that goes beyond detectable anatomical changes. We propose that omics technologies as precision medicine tools are well suited to provide insight into its pathophysiology and provide diagnostic markers and therapeutic targets.

Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure.

Purpose: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment.

A novel approach to modeling multifactorial diseases using Ensemble Bayesian Rule classifiers.

Modeling factors influencing disease phenotypes, from biomarker profiling study datasets, is a critical task in biomedicine. Such datasets are typically generated from high-throughput 'omic' technologies, which help examine disease mechanisms at an unprecedented resolution. These datasets are challenging because they are high-dimensional.

Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models.

Finding optimal blood pressure (BP) target and BP treatment after acute ischemic or hemorrhagic strokes is an area of controversy and a significant unmet need in the critical care of stroke victims. Numerous large prospective clinical trials have been done to address this question but have generated neutral or conflicting results. One major limitation that may have contributed to so many neutral or conflicting clinical trial results is the "one-size fit all" approach to BP targets, while the optimal BP target likely varies between individuals.

Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery.

Aim: To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.

Methods: Bayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks (BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs.

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance.

Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain.

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size.

Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. The predictive modeling of such microbiota count data for the classification of human infection from parasitic worms, such as helminths, can help in the detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse, containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed.

On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.

Background: Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations.

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification.

Background: Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.

Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.

In this era of precision medicine, understanding the epigenetic differences in lung cancer subtypes could lead to personalized therapies by possibly reversing these alterations. Traditional methods for analyzing microarray data rely on the use of known pathways. We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data.

Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data.

Background: Most 'transcriptomic' data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, 'transcriptomic' data may help build better classification models. However, most proposed methods for integrative analysis of 'transcriptomic' data cannot incorporate domain knowledge, which can improve model performance.

Selective model averaging with bayesian rule learning for predictive biomedicine.

Accurate disease classification and biomarker discovery remain challenging tasks in biomedicine. In this paper, we develop and test a practical approach to combining evidence from multiple models when making predictions using selective Bayesian model averaging of probabilistic rules. This method is implemented within a Bayesian Rule Learning system and compared to model selection when applied to twelve biomedical datasets using the area under the ROC curve measure of performance.

sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.

A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction.

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids.

Background: Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.

Methodology: A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.

Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma.

Background: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.

Creating a pipeline of talent for informatics: STEM initiative for high school students in computer science, biology, and biomedical informatics.

This editorial provides insights into how informatics can attract highly trained students by involving them in science, technology, engineering, and math (STEM) training at the high school level and continuing to provide mentorship and research opportunities through the formative years of their education. Our central premise is that the trajectory necessary to be expert in the emergent fields in front of them requires acceleration at an early time point. Both pathology (and biomedical) informatics are new disciplines which would benefit from involvement by students at an early stage of their education.

Novel MRI-derived quantitative biomarker for cardiac function applied to classifying ischemic cardiomyopathy within a Bayesian rule learning framework.

Characterization of regional left ventricular (LV) function may have application in prognosticating timely response and informing choice therapy in patients with ischemic cardiomyopathy. The purpose of this study is to characterize LV function through a systematic analysis of 4D (3D + time) endocardial motion over the cardiac cycle in an effort to define objective, clinically useful metrics of pathological remodeling and declining cardiac performance, using standard cardiac MRI data for two distinct patient cohorts accessed from CardiacAtlas.org: a) MESA - a cohort of asymptomatic patients; and b) DETERMINE - a cohort of symptomatic patients with a history of ischemic heart disease (IHD) or myocardial infarction.

Application of Bayesian logistic regression to mining biomedical data.

Mining high dimensional biomedical data with existing classifiers is challenging and the predictions are often inaccurate. We investigated the use of Bayesian Logistic Regression (B-LR) for mining such data to predict and classify various disease conditions. The analysis was done on twelve biomedical datasets with binary class variables and the performance of B-LR was compared to those from other popular classifiers on these datasets with 10-fold cross validation using the WEKA data mining toolkit.

An empirical workflow for genome-wide single nucleotide polymorphism-based predictive modeling.

Technology is constantly evolving, necessitating the development of workflows for efficient use of high-dimensional data. We develop and test an empirical workflow for predictive modeling based on single nucleotide polymorphisms (SNP) from genome-wide association study (GWAS) datasets. To this aim, we use as a case study SNP-based prediction of survival for non-small cell lung cancer (NSCLC) with a Bayesian rule learner system (BRL+).

Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.

Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications.

A Partitioning Based Adaptive Method for Robust Removal of Irrelevant Features from High-dimensional Biomedical Datasets.

We propose a novel method called Partitioning based Adaptive Irrelevant Feature Eliminator (PAIFE) for dimensionality reduction in high-dimensional biomedical datasets. PAIFE evaluates feature-target relationships over not only a whole dataset, but also the partitioned subsets and is extremely effective in identifying features whose relevancies to the target are conditional on certain other features. PAIFE adaptively employs the most appropriate feature evaluation strategy, statistical test and parameter instantiation.

A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening.

Introduction: Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules.

Methods: To identify candidate serum biomarkers, we measured 70 cancer-related proteins by Luminex xMAP (Luminex Corporation) multiplexed immunoassays in a training set of sera from 56 patients with biopsy-proven primary non-small-cell lung cancer and 56 age-, sex-, and smoking-matched CT-screened controls.

Results: We identified a panel of 10 serum biomarkers-prolactin, transthyretin, thrombospondin-1, E-selectin, C-C motif chemokine 5, macrophage migration inhibitory factor, plasminogen activator inhibitor, receptor tyrosine-protein kinase, erbb-2, cytokeratin fragment 21.