Knowing me, knowing you-A study on top-down requirements for compensatory scanning in drivers with homonymous visual field loss.

OBJECTIVE: It is currently still unknown why some drivers with visual field loss can compensate well for their visual impairment while others adopt ineffective strategies. This paper contributes to the methodological investigation of the associated top-down mechanisms and aims at validating a theoretical model on the requirements for successful compensation among drivers with homonymous visual field loss. METHODS: A driving simulator study was conducted with eight participants with homonymous visual field loss and eight participants with normal vision. Participants drove through an urban surrounding and experienced a baseline scenario and scenarios with visual precursors indicating increased likelihoods of crossing hazards. Novel measures for the assessment of the mental model of their visual abilities, the mental model of the driving scene and the perceived attention demand were developed and used to investigate the top-down mechanisms behind attention allocation and hazard avoidance. RESULTS: Participants with an overestimation of their visual field size tended to prioritize their seeing side over their blind side both in subjective and objective measures. The mental model of the driving scene showed close relations to the subjective and actual attention allocation. While participants with homonymous visual field loss were less anticipatory in their usage of the visual precursors and showed poorer performances compared to participants with normal vision, the results indicate a stronger reliance on top-down mechanism for drivers with visual impairments. A subjective focus on the seeing side or on near peripheries more frequently led to bad performances in terms of collisions with crossing cyclists. CONCLUSION: The study yielded promising indicators for the potential of novel measures to elucidate top-down mechanisms in drivers with homonymous visual field loss. Furthermore, the results largely support the model of requirements for successful compensatory scanning. The findings highlight the importance of individualized interventions and driver assistance systems tailored to address these mechanisms.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing.

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

SIMON: Open-Source Knowledge Discovery Platform.

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

The advantages of describing covalent inhibitor in vitro potencies by IC50 at a fixed time point. IC50 determination of covalent inhibitors provides meaningful data to medicinal chemistry for SAR optimization.

Recent years have seen a resurgence in drug discovery efforts aimed at the identification of covalent inhibitors which has led to an explosion of literature reports in this area and most importantly new approved therapies. These reports and breakthroughs highlight the significant investments made across the industry in SAR campaigns to optimize inhibitors. The potency of covalent inhibitors is generally considered to be more accurately described by the time-independent kinetic parameter kinact/Ki rather than a by a simple IC50 since the latter is a time-dependent parameter. Enzyme substrate concentrations are an additional important factor to consider when attempting to translate parameters derived from enzymology experiments to phenotypic behavior in a physiologically relevant cell-based system. Theoretical and experimental investigations into the relationship between IC50, time, substrate concentration and Kinact/Ki provided us with an effective approach to provide meaningful data for SAR optimization. The data we generated for our JAK3 irreversible covalent inhibitor program using IC50 values provided by enzyme assays with long incubations (>1h) coupled with physiological substrate concentration provided the medicinal chemist with optimal information in a rapid and efficient manner. We further document the wide applicability of this method by applying it to other enzymes systems where we have run covalent inhibitor programs.

Accurate and fast feature selection workflow for high-dimensional omics data.

We are moving into the age of 'Big Data' in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S * F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical omics classification includes redundant and irrelevant features (e.g. genes or proteins) that can result in long computation times; decrease of the model performance and the selection of suboptimal features (genes and proteins) after the classification/regression step. Multiple algorithms and reviews has been published to describe all the existing methods for feature selection, their strengths and weakness. However, the selection of the correct FS algorithm and strategy constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a 'grey zone'. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domain's problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.

Ultra-Sensitive Measurement of IL-17A and IL-17F in Psoriasis Patient Serum and Skin.

Interleukin 17 is a family of cytokines that play a central role in many autoimmune and inflammatory diseases. IL-17A has been implicated as a key driver of psoriasis, mediating a chronic cycle of T-cell activation, keratinocyte proliferation and angiogenesis. It has been hypothesized that expression of IL-17A and the related cytokine IL-17F could be used as predictive biomarkers for therapeutic response, though they have been difficult to measure locally or in circulation because of their low abundance. We developed ultrasensitive methods for measuring IL-17A and IL-17F in human serum samples and found that serum from psoriasis patients had higher and a broader range of concentrations of both IL-17 proteins compared to healthy volunteers. We also adapted these methods for tissue biopsies and saw higher concentrations of both IL-17 proteins in psoriatic lesions, but they were undetectable in non-lesional skin from the same patients.

Leveraging of Rifampicin-Dosed Cynomolgus Monkeys to Identify Bile Acid 3-O-Sulfate Conjugates as Potential Novel Biomarkers for Organic Anion-Transporting Polypeptides.

In the search for novel bile acid (BA) biomarkers of liver organic anion-transporting polypeptides (OATPs), cynomolgus monkeys received oral rifampicin (RIF) at four dose levels (1, 3, 10, and 30 mg/kg) that generated plasma-free Cmax values (0.06, 0.66, 2.57, and 7.79 µM, respectively) spanning the reported in vitro IC50 values for OATP1B1 and OATP1B3 (≤1.7 µM). As expected, the area under the plasma concentration-time curve (AUC) of an OATP probe drug (i.v. 2H4-pitavastatin, 0.2 mg/kg) was increased 1.2-, 2.4-, 3.8-, and 4.5-fold, respectively. Plasma of RIF-dosed cynomolgus monkeys was subjected to a liquid chromatography-tandem mass spectrometry method that supported the analysis of 30 different BAs. Monkey urine was profiled, and we also determined that the impact of RIF on BA renal clearance was minimal. Although sulfated BAs comprised only 1% of the plasma BA pool, a robust RIF dose response (maximal ≥50-fold increase in plasma AUC) was observed for the sulfates of five BAs [glycodeoxycholate (GDCA-S), glycochenodeoxycholate (GCDCA-S), taurochenodeoxycholate, deoxycholate (DCA-S), and taurodeoxycholate (TDCA-S)]. In vitro, RIF (≤100 µM) did not inhibit cynomolgus monkey liver cytosol-catalyzed BA sulfation and cynomolgus monkey hepatocyte-mediated uptake of representative sulfated BAs (GDCA-S, GCDCA-S, DCA-S, and TDCA-S) was sodium-independent and inhibited (≥70%) by RIF (5 µM); uptake of taurocholic acid was sensitive to sodium removal (74% decrease) and relatively refractory to RIF (≤21% inhibition). We concluded that sulfated BAs may serve as sensitive biomarkers of cynomolgus monkey OATPs and that exploration of their utility as circulating human OATP biomarkers is warranted.

Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

PURPOSE: This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). METHODS: The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. FINDINGS: The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14.1%), baseline depression (13.9%), and using pregabalin as a monotherapy (11.7%). IMPLICATIONS: The finding that pain changes by week 1 or weeks 1 and 3 are the best predictors of pregabalin response at 6 weeks suggests that adhering to a pregabalin medication regimen is important for an optimal end-of-treatment outcome. Regarding baseline predictors alone, considerable published evidence supports the importance of high baseline pain score and presence of depression as factors that can affect treatment response. Future research would be required to elucidate why using pregabalin as a monotherapy also had more than a 10% variable importance as a potential predictor.

Quantitative Assessment of the Impact of Fluorine Substitution on P-Glycoprotein (P-gp) Mediated Efflux, Permeability, Lipophilicity, and Metabolic Stability.

Strategic replacement of one or more hydrogen atoms with fluorine atom(s) is a common tactic to improve potency at a given target and/or to modulate parameters such as metabolic stability and pKa. Molecular weight (MW) is a key parameter in design, and incorporation of fluorine is associated with a disproportionate increase in MW considering the van der Waals radius of fluorine versus hydrogen. Herein we examine a large compound data set to understand the effect of introducing fluorine on the risk of encountering P-glycoprotein mediated efflux (as measured by MDR efflux ratio), passive permeability, lipophilicity, and metabolic stability. Statistical modeling of the MDR ER data demonstrated that an increase in MW as a result of introducing fluorine atoms does not lead to higher risk of P-gp mediated efflux. Fluorine-corrected molecular weight (MWFC), where the molecular weight of fluorine has been subtracted, was found to be a more relevant descriptor.

Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records.

BACKGROUND: Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed. METHODS: Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions. RESULTS: The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model's predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of an FM diagnosis and to identify no-FM subjects. CONCLUSION: Random forest modeling may help to quantify the predictive probability of an FM diagnosis. Rules can be developed to simplify interpretability. Further validation of these models may facilitate earlier diagnosis and enhance management.

Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization.

BACKGROUND: Diagnosis of fibromyalgia (FM) is often challenging. Identifying factors associated with an FM diagnosis may guide health care providers in implementing appropriate diagnostic and management strategies. METHODS: This retrospective study used the de-identified Humedica electronic medical record (EMR) database to identify variables associated with an FM diagnosis. Cases (n=4,296) were subjects ≥18 years old with ≥2 International Classification of Diseases, Ninth Revision (ICD-9) codes for FM (729.1) ≥30 days apart during 2012, associated with an integrated delivery network, with ≥1 encounter with a health care provider in 2011 and 2012. Controls without FM (no-FM; n=583,665) did not have the ICD-9 codes for FM. Demographic, clinical, and health care resource utilization variables were extracted from structured EMR data. Univariate analysis identified variables showing significant differences between the cohorts based on odds ratios (ORs). RESULTS: Consistent with FM epidemiology, FM subjects were predominantly female (78.7% vs 64.5%; P<0.0001) and slightly older (mean age 53.3 vs 52.7 years; P=0.0318). Relative to the no-FM cohort, the FM cohort was characterized by a higher prevalence of nearly all evaluated comorbidities; the ORs suggested a higher likelihood of an FM diagnosis (P<0.0001), especially for musculoskeletal and neuropathic pain conditions (OR 3.1 for each condition). Variables potentially associated with an FM diagnosis included higher levels of use of specific health care resources including emergency-room visits, outpatient visits, hospitalizations, and medications. Units used per subject for emergency-room visits, outpatient visits, hospitalizations, and medications were also significantly higher in the FM cohort (P<0.0001), confirming resource utilization as an important variable associated with an FM diagnosis. CONCLUSION: Significant differences between the FM and no-FM cohorts were observed for nearly all the demographic, clinical, and health care resource variables, suggesting an association with FM diagnosis. These results also support use of EMR data for identifying variables associated with FM, which may help in the diagnosis and management of this condition.

Active glucagon-like peptide 1 quantitation in human plasma: a comparison of multiple ligand binding assay platforms.

There are a wide variety of ligand binding assay platforms available for implementation in present day bioanalytical laboratories. Selecting the platform that best suits a particular project's needs is highly dependent upon multiple assay characteristics. The active form of glucagon-like protein (GLP-1) is a biomarker of interest for type 2 diabetes (T2DM), and therefore a common target for quantitation. Previous projects requiring active GLP-1 measurements involved the use of a labor intensive ELISA, spurring an investigation towards other potential assay platforms. To that end, four separate ligand binding assay formats (standard ELISA, electrochemiluminescence, Gyrolab, and Singulex) were evaluated. The platforms were compared for numerous assay parameters including dynamic range, sample volume requirements, throughput, and cost. Additionally, thirty individual donor plasmas were run with each assay as representative study samples. Although our evaluation did not show any platform that was better than others in all assay characteristics, there was one that was best in sensitivity (Singulex) and one that was best in throughput and sample volume requirements (Gyrolab). The lack of a technology that was best in all categories underscores the importance of due diligence when selecting an assay platform; there are no silver bullets, and one must take into account what is necessary for project needs and the intended use of the data.

More than just hormones: H295R cells as predictors of reproductive toxicity.

Many of the commonly observed reproductive toxicities associated with therapeutic compounds can be traced to a disruption of the steroidogenic pathway. We sought to develop an in vitro assay that would predict reproductive toxicity and be high throughput in nature. H295R cells, previously validated as having an intact and functional steroidogenic pathway, were treated with 83 known-positive and 79 known-negative proprietary and public-domain compounds. The assay measured the expression of the key enzymes STAR, 3ßHSD2, CYP17A1, CYP11B2, CYP19A1, CYP21A2, and CYP11A1 and the hormones DHEA, progesterone, testosterone, and cortisol. We found that a Random Forest model yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.845, with sensitivity of 0.724 and specificity of 0.758 for predicting in vivo reproductive toxicity with this in vitro assay system.

Improving the odds of success in drug discovery: choosing the best compounds for in vivo toxicology studies.

A set of molecules that advanced into exploratory animal toxicology studies (two species) was examined to determine what properties contributed to success in these safety studies. Compounds were rigorously evaluated across numerous safety end points and classified as "pass" if a suitable in vivo therapeutic index (TI) was achieved for advancement into regulatory toxicology studies. The most predictive end point contributing to compound survival was a predicted human efficacious concentration (Ceff) of ≤250 nM (total drug) and ≤40 nM (free drug). This trend held across a wide range of CNS modes of action, encompassing targets such as enzymes, G-protein-coupled receptors, ion channels, and transporters.

[Scores and stages in pneumology]. / Scores und Stadien in der Pneumologie.

Useful scales and classifications for patients with pulmonary diseases are discussed. The modified Medical Research Council breathlessness scale (mMRC) is a measure of disability in lung patients. The GOLD classifications, the COPD-Assessment Test (CAT) and the BODE Index are important to classify the severity of COPD and to measure the disability of these patients. The Geneva score is a clinical prediction rule used in determining the pre-test probability of pulmonary embolism. The Pulmonary Embolism Severity Index (PESI) is a scoring system used to predict 30 day mortality in patients with pulmonary embolism. The Epworth Sleepiness Scale is intended to measure daytime sleepiness in patients with sleep apnea syndrome. The Asthma Controll Test (ACT) determines if asthma symptoms are well controlled.

The use of the R language for medicinal chemistry applications.

This manuscript serves as a review of how the R language has been used in the last decade to address problems related to medicinal chemistry design. This includes the use of the R language for chemoinformatics applications and interfaces, as well as statistical modeling for ADMET and potency endpoints. Additionally, a few examples of R code are provided to demonstrate the ability of this language to make available cutting-edge statistical analysis to the medicinal chemistry design community.

Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease.

OBJECTIVES: While plasma biomarkers have been proposed to aid in the clinical diagnosis of Alzheimer disease (AD), few biomarkers have been validated in independent patient cohorts. Here we aim to determine plasma biomarkers associated with AD in 2 independent cohorts and validate the findings in the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI). METHODS: Using a targeted proteomic approach, we measured levels of 190 plasma proteins and peptides in 600 participants from 2 independent centers (University of Pennsylvania, Philadelphia; Washington University, St. Louis, MO), and identified 17 analytes associated with the diagnosis of very mild dementia/mild cognitive impairment (MCI) or AD. Four analytes (apoE, B-type natriuretic peptide, C-reactive protein, pancreatic polypeptide) were also found to be altered in clinical MCI/AD in the ADNI cohort (n = 566). Regression analysis showed CSF Aß42 levels and t-tau/Aß42 ratios to correlate with the number of APOE4 alleles and plasma levels of B-type natriuretic peptide and pancreatic polypeptide. CONCLUSION: Four plasma analytes were consistently associated with the diagnosis of very mild dementia/MCI/AD in 3 independent clinical cohorts. These plasma biomarkers may predict underlying AD through their association with CSF AD biomarkers, and the association between plasma and CSF amyloid biomarkers needs to be confirmed in a prospective study.

Plasma biomarkers associated with the apolipoprotein E genotype and Alzheimer disease.

BACKGROUND: A blood-based test that could be used as a screen for Alzheimer disease (AD) may enable early intervention and better access to treatment. OBJECTIVE: To apply a multiplex immunoassay panel to identify plasma biomarkers of AD using plasma samples from the Alzheimer's Disease Neuroimaging Initiative cohort. DESIGN: Cohort study. SETTING: The Biomarkers Consortium Alzheimer's Disease Plasma Proteomics Project. PARTICIPANTS: Plasma samples at baseline and at 1 year were analyzed from 396 (345 at 1 year) patients with mild cognitive impairment, 112 (97 at 1 year) patients with AD, and 58 (54 at 1 year) healthy control subjects. MAIN OUTCOME MEASURES: Multivariate and univariate statistical analyses were used to examine differences across diagnostic groups and relative to the apolipoprotein E (ApoE) genotype. RESULTS: Increased levels of eotaxin 3, pancreatic polypeptide, and N-terminal protein B-type brain natriuretic peptide were observed in patients, confirming similar changes reported in cerebrospinal fluid samples of patients with AD and MCI. Increases in tenascin C levels and decreases in IgM and ApoE levels were also observed. All participants with Apo ε3/ε4 or ε4/ε4 alleles showed a distinct biochemical profile characterized by low C-reactive protein and ApoE levels and by high cortisol, interleukin 13, apolipoprotein B, and gamma interferon levels. The use of plasma biomarkers improved specificity in differentiating patients with AD from controls, and ApoE plasma levels were lowest in patients whose mild cognitive impairment had progressed to dementia. CONCLUSIONS: Plasma biomarker results confirm cerebrospinal fluid studies reporting increased levels of pancreatic polypeptide and N-terminal protein B-type brain natriuretic peptide in patients with AD and mild cognitive impairment. Incorporation of plasma biomarkers yielded high sensitivity with improved specificity, supporting their usefulness as a screening tool. The ApoE genotype was associated with a unique biochemical profile irrespective of diagnosis, highlighting the importance of genotype on blood protein profiles.

Silencing of enzymes involved in ceramide biosynthesis causes distinct global alterations of lipid homeostasis and gene expression.

Dysregulation of ceramide synthesis has been associated with metabolic disorders such as atherosclerosis and diabetes. We examined the changes in lipid homeostasis and gene expression in Huh7 hepatocytes when the synthesis of ceramide is perturbed by knocking down serine pal mitoyltransferase subunits 1, 2, and 3 (SPTLC123) or dihydroceramide desaturase 1 (DEGS1). Although knocking down all SPTLC subunits is necessary to reduce total ceramides significantly, depleting DEGS1 is sufficient to produce a similar outcome. Lipidomic analysis of distribution and speciation of multiple lipid classes indicates an increase in phospholipids in SPTLC123-silenced cells, whereas DEGS1 depletion leads to the accumulation of sphingolipid intermediates, free fatty acids, and diacylglycerol. When cer amide synthesis is disrupted, the transcriptional profiles indicate inhibition in biosynthetic processes, downregulation of genes involved in general endomembrane trafficking, and upregulation of endocytosis and endosomal recycling. SPTLC123 silencing strongly affects the expression of genes involved with lipid metabolism. Changes in amino acid, sugar, and nucleotide metabolism, as well as vesicle trafficking between organelles, are more prominent in DEGS1-silenced cells. These studies are the first to provide a direct and comprehensive understanding at the lipidomic and transcriptomic levels of how Huh7 hepatocytes respond to changes in the inhibition of ceramide synthesis.

Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform.

Triglyceride accumulation is associated with obesity and type 2 diabetes. Genetic disruption of diacylglycerol acyltransferase 1 (DGAT1), which catalyzes the final reaction of triglyceride synthesis, confers dramatic resistance to high-fat diet induced obesity. Hence, DGAT1 is considered a potential therapeutic target for treating obesity and related metabolic disorders. However, the molecular events shaping the mechanism of action of DGAT1 pharmacological inhibition have not been fully explored yet. Here, we investigate the metabolic molecular mechanisms induced in response to pharmacological inhibition of DGAT1 using a recently developed computational systems biology approach, the Causal Reasoning Engine (CRE). The CRE algorithm utilizes microarray transcriptomic data and causal statements derived from the biomedical literature to infer upstream molecular events driving these transcriptional changes. The inferred upstream events (also called hypotheses) are aggregated into biological models using a set of analytical tools that allow for evaluation and integration of the hypotheses in context of their supporting evidence. In comparison to gene ontology enrichment analysis which pointed to high-level changes in metabolic processes, the CRE results provide detailed molecular hypotheses to explain the measured transcriptional changes. CRE analysis of gene expression changes in high fat habituated rats treated with a potent and selective DGAT1 inhibitor demonstrate that the majority of transcriptomic changes support a metabolic network indicative of reversal of high fat diet effects that includes a number of molecular hypotheses such as PPARG, HNF4A and SREBPs. Finally, the CRE-generated molecular hypotheses from DGAT1 inhibitor treated rats were found to capture the major molecular characteristics of DGAT1 deficient mice, supporting a phenotype of decreased lipid and increased insulin sensitivity.