Eric F Lock

• PhD
• Professor (Associate) at University of Minnesota

Objective We aimed to quantify differences in the brain and spinal cord between Friedreich ataxia and controls, stratified by age and disease stage, including for the first time in young children. Methods TRACK‐FA is the largest prospective, longitudinal, multi‐modal neuroimaging study in Friedreich ataxia to date. We assessed individuals with Fri...

In biomedical research and other fields, it is now common to generate high content data that are both multi-source and multi-way. Multi-source data are collected from different high-throughput technologies while multi-way data are collected over multiple dimensions, yielding multiple tensor arrays. Integrative analysis of these data sets is needed,...

Often in educational and psychological studies, researchers are interested in understanding the mediation mechanism of longitudinal (repeated measures) variables. Almost all longitudinal mediation models in the literature stem from structural equation modeling framework and hence, cannot directly estimate intrinsically nonlinear functions (e.g., ex...

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, ex...

Importance Gestational diabetes (GD) is linked to health risks for the birthing parent and infant. The outcomes of GD on human milk composition are mostly unknown. Objective To determine associations between GD, the human milk metabolome, and infant growth and body composition. Design, Setting, and Participants Cohort study using data from the Mo...

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, ex...

Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, ex...

Rationale Obstructive lung disease is increasingly common among persons living with HIV (PLWH). There are currently no validated biomarkers that identify individuals at risk of developing obstructive lung disease (OLD) and specific mechanisms contributing to HIV-associated OLD remain elusive, independent of smoking. Objectives We sought to identif...

Background Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear. Methods We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage flu...

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis b...

Human milk is a complex mix of nutritional and bioactive components that provide complete nourishment for the infant. However, we lack a systematic knowledge of the factors shaping milk composition and how milk variation influences infant health. Here, we characterize relationships between maternal genetics, milk gene expression, milk composition,...

Data for several applications in diverse fields can be represented as multiple matrices that are linked across rows or columns. This is particularly common in molecular biomedical research, in which multiple molecular “omics” technologies may capture different feature sets (e.g., corresponding to rows in a matrix) and/or different sample population...

Data for several applications in diverse fields can be represented as multiple matrices that are linked across rows or columns. This is particularly common in molecular biomedical research, in which multiple molecular "omics" technologies may capture different feature sets (e.g., corresponding to rows in a matrix) and/or different sample population...

Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the...

Purpose: To develop a dentate nucleus (DN) segmentation tool using deep learning (DL) applied to brain quantitative susceptibility mapping (QSM) images. Materials and Methods: Brain QSM images from 132 healthy controls and 170 individuals with cerebellar ataxia or multiple sclerosis were collected from nine different datasets worldwide for this ret...

Background: Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear. Methods: We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage fl...

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a...

Crossed random effects models (CREMs) are particularly useful in longitudinal data applications because they allow researchers to account for the impact of dynamic group membership on individual outcomes. However, no research has determined what data conditions need to be met to sufficiently identify these models, especially the group effects, in a...

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR),...

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR),...

Background: Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. Objective: Assess intranasal insulin administr...

Background Observational studies have shown an association between higher bilirubin levels and improved respiratory health outcomes. Targeting higher bilirubin levels has been proposed as a novel therapeutic strategy in COPD. However, bilirubin levels are influenced by multiple intrinsic and extrinsic factors, and these observational studies are pr...

Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the...

Introduction Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early pr...

Objective: Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years. Research design and methods: This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a co...

A Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e., multidimensional tensor) structure is described. As a motivating example, molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron defici...

The relation between reading and mathematics achievement is an active area of research, and evidence in the extant literature suggests they are closely related. However, there is a lack of understanding about how their relation develops over time. Indeed, traditional longitudinal analyses have focused on investigating each domain independently, whi...

Chronic obstructive pulmonary disease (COPD) is among the leading causes of death worldwide and HIV is an independent risk factor for the development of COPD. However, the etiology of this increased risk and means to identify persons with HIV (PWH) at highest risk for COPD have remained elusive. Biomarkers may reveal etiologic pathways and allow be...

Human milk is a complex mix of nutritional and bioactive components that provide complete nutrition for the infant. However, we lack a systematic knowledge of the factors shaping milk composition and how milk variation influences infant health. Here, we used multi-omic profiling to characterize interactions between maternal genetics, milk gene expr...

Purpose Obstructive lung disease is increasingly common among persons with HIV in both smokers and non-smokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease. Methods Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were ma...

Background: Infantile iron deficiency (ID) causes anemia and compromises neurodevelopment. Current screening relies on hemoglobin (Hgb) determination at 1 year of age, which lacks sensitivity and specificity for timely detection of infantile ID. Low reticulocyte Hgb equivalent (RET-He) indicates ID, but its predictive accuracy relative to conventi...

Understanding of the pathophysiology of obstructive lung disease (OLD) is limited by available methods to examine the relationship between multi-omic molecular phenomena and clinical outcomes. Integrative factorization methods for multi-omic data can reveal latent patterns of variation describing important biological signal. However, most methods d...

Introduction Drug development for neurodegenerative diseases such as Friedreich’s ataxia (FRDA) is limited by a lack of validated, sensitive biomarkers of pharmacodynamic response in affected tissue and disease progression. Studies employing neuroimaging measures to track FRDA have thus far been limited by their small sample sizes and limited follo...

The effects of early-life iron deficiency anemia (IDA) extend past the blood and include both short- and long-term adverse effects on many tissues including the brain. Prior to IDA, iron deficiency (ID) can cause similar tissue effects, but a sensitive biomarker of iron-dependent brain health is lacking. To determine serum and CSF biomarkers of ID-...

We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e.. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life ir...

Background: HIV is a risk factor for obstructive lung disease (OLD), independent of smoking. We used mass spectrometry (MS) approaches to identify metabolomic biomarkers that inform mechanistic pathogenesis of OLD in persons with HIV (PWH). Methods: We obtained bronchoalveolar lavage fluid (BALF) samples from 52 PWH, in case:control (+OLD/-OLD)...

Modern data often take the form of a multiway array. However, most classification methods are designed for vectors, that is, one-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway stru...

Background Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of pred...

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal sample...

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all s...

Distance weighted discrimination (DWD) is a linear discrimination method that is particularly well-suited for classification tasks with high-dimensional data. The DWD coefficients minimize an intuitive objective function, which can solved efficiently using state-of-the-art optimization techniques. However, DWD has not yet been cast into a model-bas...

Background: The effects of iron deficiency (ID) during infancy extend beyond the hematologic compartment and include short- and long-term adverse effects on many tissues including the brain. However, sensitive biomarkers of iron-dependent brain health are lacking in humans. Objective: To determine whether serum and CSF biomarkers of ID-induced m...

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogeneity beyond what was observed in single tumor...

A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess...

Modern data often take the form of a multiway array. However, most classification methods are designed for vectors, i.e., 1-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway structure...

While the gut microbiome and host gene regulation separately contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here, we developed a machine learning-based framework to jointly analyze host transcriptomic and microbiome profiles from 416 colonic mucosal samples of patients with colore...

We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data. Our outcome is the number of diseased sites measured over four different tooth types for each subject, with subject‐specific covariates available as predictors. The outcomes are not well‐cha...

Background: The effects of infantile iron deficiency anemia (IDA) extend beyond hematological indices and include short- and long-term adverse effects on multiple cells and tissues. IDA is associated with an abnormal serum metabolomic profile, characterized by altered hepatic metabolism, lowered NAD flux, increased nucleoside levels, and a reducti...

Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer, expanding what was known from single-cancer or single-omics studies. However, pan-cancer, pan-omics analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g...

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all s...

Background: Pre-anemic iron deficiency (ID) and ID anemia in infancy is associated with long-term effects on brain development. Iron is an essential micronutrient needed for multiple neurodevelopmental processes, including energy production, myelination and neurotransmission. Serum and cerebrospinal fluid (CSF) metabolomic and proteomic analyses ca...

Longitudinal processes rarely occur in isolation; often the growth curves of two or more variables are interdependent. Moreover, growth curves rarely exhibit a constant pattern of change. Many educational and psychological phenomena are comprised of different developmental phases (segments). Bivariate piecewise linear mixed-effects models (BPLMEM)...

Distance weighted discrimination (DWD) is a linear discrimination method that is particularly well-suited for classification tasks with high-dimensional data. The DWD coefficients minimize an intuitive objective function, which can solved very efficiently using state-of-the-art optimization techniques. However, DWD has not yet been cast into a mode...

In Idiopathic Pulmonary Fibrosis (IPF), there is unrelenting scarring of the lung mediated by pathological mesenchymal progenitor cells (MPCs) that manifest autonomous fibrogenicity in xenograft models. To determine where along their differentiation trajectory IPF MPCs acquire fibrogenic properties, we analyzed the transcriptome of 335 MPCs isolate...

Objectives To determine whether rapid correction of iron deficiency using intramuscular iron dextran normalizes serum metabolomic changes in a nonhuman primate model of iron deficiency anemia (IDA). Methods Blood was collected from naturally iron-sufficient (IS; n = 10) and IDA (n = 12) male and female infant rhesus monkeys (Macaca mulatta) at 6 m...

We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to “borrow” information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on s...

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogenity beyond what was observed in single tumor a...

Motivation: The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for...

Background: Iron deficiency is the most common nutrient deficiency in human infants aged 6 to 24 mo, and negatively affects many cellular metabolic processes, including energy production, electron transport, and oxidative degradation of toxins. There can be persistent influences on long-term metabolic health beyond its acute effects. Objectives:...

A popular method for estimating a causal treatment effect with observational data is the difference-in-differences (DiD) model. In this work, we consider an extension of the classical DiD setting to the hierarchical context in which data cannot be matched at the most granular level (e.g., individual-level differences are unobservable). We propose a...

We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on s...

Advances in molecular “omics” technologies have motivated new methodology for the integration of multiple sources of high‐content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform)....

Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform)....

Although national measures of the quality of diabetes care delivery demonstrate improvement, progress has been slow. In 2008, the Minnesota legislature endorsed the patient-centered medical home (PCMH) as the preferred model for primary care redesign. In this work, we investigate the effect of PCMH-related clinic redesign and resources on diabetes...

Purpose: Preterm infants <29 weeks of gestation are at risk for severe intraventricular hemorrhage (IVH). Lower gestational age, birth weight, severe illness, as indexed by higher Score for Neonatal Acute Physiology - Perinatal Extension II (SNAPPE-II) are associated with severe IVH. The role of coagulation abnormalities on the first day after birt...

We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data. Our outcome is the number of diseased sites measured over four different tooth types for each subject, with subject-specific covariates available as predictors. The outcomes are not well-cha...

High-dimensional multi-source data are encountered in many fields. Despite recent developments on the integrative dimension reduction of such data, most existing methods cannot easily accommodate data of multiple types (e.g. binary or count-valued). Moreover, multi-source data often have block-wise missing structure, i.e. data in one or more source...

Piecewise growth mixture models (PGMM) are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional for...

High-dimensional multi-source data are encountered in many fields. Despite recent developments on the integrative dimension reduction of such data, most existing methods cannot easily accommodate data of multiple types (e.g., binary or count-valued). Moreover, multi-source data often have block-wise missing structure, i.e., data in one or more sour...

Introduction: Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesize that the COPD stroma contains molecular mechanisms supporting tumorigenesis. Materials/methods: We conducted an unbiased multi-omic analysis to identify gene expression patterns...

We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates auxiliary covariates, SupCP. SupCP generalizes the supervised singular value decomposition (SupSVD) for vector-valued observations, to allow for observations that have the form of a matrix or higher-order array. Such data are increasi...

Sea turtles are a keystone species and are highly sensitive to changes in their environment, making them excellent environmental indicators. In light of environmental and climate changes, species are increasingly threatened by pollution, changes in ocean health, habitat alteration, and plastic ingestion. There may be additional health related threa...

Several recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set wit...

Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal components analysis (PCA). However, the application of PCA is not straightforward for multi-source data, wherein multiple sources of 'omics data measure different but related biological components. In this article we utilize rec...

Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal components analysis (PCA). However, the application of PCA is not straightforward for multi-source data, wherein multiple sources of 'omics data measure different but related biological components. In this article we utilize rec...

We propose a framework for the linear prediction of a multi-way array (i.e., a tensor) from another multi-way array of arbitrary dimension, using the contracted tensor product. This framework generalizes several existing approaches, including methods to predict a scalar outcome from a tensor, a matrix from a matrix, or a tensor from a scalar. We de...

We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates auxiliary covariates, SupCP. SupCP generalizes the supervised singular value decomposition (SupSVD) for vector-valued observations, to allow for observations that have the form of a matrix or higher-order array. Such data are increasi...

Objectives: Iron deficiency (ID) anemia leads to long-term neurodevelopmental deficits by altering iron-dependent brain metabolism. The objective of the study was to determine if ID induces metabolomic abnormalities in the cerebrospinal fluid (CSF) in the pre-anemic stage and to ascertain the aspects of abnormal brain metabolism affected. Methods...

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In pra...

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In pra...

The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. JIVE is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank ap...

High-throughput genetic and epigenetic data are often screened for associations with an observed phenotype. For example, one may wish to test hundreds of thousands of genetic variants, or DNA methylation sites, for an association with disease status. These genomic variables can naturally be grouped by the gene they encode, among other criteria. How...

This is a reproducible, documented workflow and code in R of data analysis of eastern pacific hawksbill sea turtle hematology and biochemistry reference ranges, and mean values for whole blood heavy metals

In biomedical research, a growing number of measurement platforms and technologies are being used to assess diverse but related information on a set of common samples. This motivates integrative methods for multisource data, in which multiple data sets are derived from a common set of objects. This chapter addresses exploratory methods for multisou...

This article concerns testing for equality of distribution between groups. We focus on screening variables with shared distributional features such as common support, modes and patterns of skewness. We propose a Bayesian testing method using kernel mixtures, which improves performance by borrowing information across the different variables and grou...

Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discover...

Background Expression quantitative trait loci (eQTL) play an important role in the regulation of gene expression. Gene expression levels and eQTLs are expected to vary from tissue to tissue, and therefore multi-tissue analyses are necessary to fully understand complex genetic conditions in humans. Dura mater tissue likely interacts with cranial bon...

Background: HDAC inhibitors (HDACi) are being investigated as treatment for relapsed/refractory non Hodgkin lymphoma (NHL) and other cancers. However, the mechanisms underlying sensitivity and resistance to HDAC inhibition in lymphomas have not been fully characterized. We probed the cellular and molecular response to HDACi in vitro and in vivo in...

Background Chiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the foramen magnum at the base of the skull, resulting in significant neurologic morbidity. As CMI patients display a high degree of clinical variability and multiple mechanisms have been proposed for tonsillar herniation, it is hypothesized...

In this study, we define the genetic landscape of mantle cell lymphoma (MCL) through exome sequencing of 56 cases of MCL. We identified recurrent mutations in ATM, CCND1, MLL2, and TP53. We further identified a number of novel genes recurrently mutated in patients with MCL including RB1, WHSC1, POT1, and SMARCA4. We noted that MCLs have a distinct...