Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction.

Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information.

Gut microbiome associations with outcome following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) in pigs immunized with a PRRS modified live virus vaccine.

Porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) are two of the most significant pathogens affecting swine. Co-infections are common and result in respiratory disease and reduced weight gain in growing pigs. Although PRRS modified live virus (MLV) vaccines are widely used to decrease PRRS-associated losses, they are generally considered inadequate for disease control.

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump.

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models.

Identification of Genome-Wide Mutations in Ciprofloxacin-Resistant F. tularensis LVS Using Whole Genome Tiling Arrays and Next Generation Sequencing.

Francisella tularensis is classified as a Class A bioterrorism agent by the U.S. government due to its high virulence and the ease with which it can be spread as an aerosol.

Microbiome associations in pigs with the best and worst clinical outcomes following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2).

On a world-wide basis, co-infections involving porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) are common and contribute to a range of polymicrobial disease syndromes in swine. Both viruses compromise host defenses, resulting in increased susceptibility to infections by primary and secondary pathogens that can affect growth performance as well as increased morbidity and mortality. An experimental population of 95 pigs was co-infected with PRRSV and PCV2.

Application of a pathogen microarray for the analysis of viruses and bacteria in clinical diagnostic samples from pigs.

Many of the disease syndromes challenging the commercial swine industry involve the analysis of complex problems caused by polymicrobial, emerging or reemerging, and transboundary pathogens. This study investigated the utility of the Lawrence Livermore Microbial Detection Array (Lawrence Livermore National Laboratory, Livermore, California), designed to detect 8,101 species of microbes, in the evaluation of known and unknown microbes in serum, oral fluid, and tonsil from pigs experimentally coinfected with Porcine reproductive and respiratory syndrome virus (PRRSV) and Porcine circovirus-2 (PCV-2). The array easily identified PRRSV and PCV-2, but at decreased sensitivities compared to standard polymerase chain reaction detection methods.

The microbial detection array for detection of emerging viruses in clinical samples--a useful panmicrobial diagnostic tool.

Emerging viruses are usually endemic to tropical and sub-tropical regions of the world, but increased global travel, climate change and changes in lifestyle are believed to contribute to the spread of these viruses into new regions. Many of these viruses cause similar disease symptoms as other emerging viruses or common infections, making these unexpected pathogens difficult to diagnose. Broad-spectrum pathogen detection microarrays containing probes for all sequenced viruses and bacteria can provide rapid identification of viruses, guiding decisions about treatment and appropriate case management.

Microbial profiling of combat wound infection through detection microarray and next-generation sequencing.

Combat wound healing and resolution are highly affected by the resident microbial flora. We therefore sought to achieve comprehensive detection of microbial populations in wounds using novel genomic technologies and bioinformatics analyses. We employed a microarray capable of detecting all sequenced pathogens for interrogation of 124 wound samples from extremity injuries in combat-injured U.

Screening of viral pathogens from pediatric ileal tissue samples after vaccination.

In 2010, researchers reported that the two US-licensed rotavirus vaccines contained DNA or DNA fragments from porcine circovirus (PCV). Although PCV, a common virus among pigs, is not thought to cause illness in humans, these findings raised several safety concerns. In this study, we sought to determine whether viruses, including PCV, could be detected in ileal tissue samples of children vaccinated with one of the two rotavirus vaccines.

Detection of Bacillus anthracis DNA in complex soil and air samples using next-generation sequencing.

Bacillus anthracis is the potentially lethal etiologic agent of anthrax disease, and is a significant concern in the realm of biodefense. One of the cornerstones of an effective biodefense strategy is the ability to detect infectious agents with a high degree of sensitivity and specificity in the context of a complex sample background. The nature of the B.

Association of Kaposi’s sarcoma-associated herpesvirus (KSHV) with bladder cancer in Croatian patients.

As the seventh most common human malignancy, bladder cancer represents a global health problem. In addition to well-recognized risk factors such as smoking and exposure to chemicals, various infectious agents have been implicated as cofactors in the pathogenesis of urothelial malignancies. The aim of the present study was to assess the possible association of viral infection and bladder cancer in Croatian patients.

Optimizing SNP microarray probe design for high accuracy microbial genotyping.

Microarrays to characterize single nucleotide polymorphisms (SNPs) provide a cost-effective and rapid method (under 24h) to genotype microbes as an alternative to sequencing. We developed a pipeline for SNP discovery and microarray design that scales to 100's of microbial genomes. Here we tested various SNP probe design strategies against 8 sequenced isolates of Bacillus anthracis to compare sequence and microarray data.

Microarrays for pathogen detection and analysis.

DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense.

A microbial detection array (MDA) for viral and bacterial detection.

Background: Identifying the bacteria and viruses present in a complex sample is useful in disease diagnostics, product safety, environmental characterization, and research. Array-based methods have proven utility to detect in a single assay at a reasonable cost any microbe from the thousands that have been sequenced.

Methods: We designed a pan-Microbial Detection Array (MDA) to detect all known viruses (including phages), bacteria and plasmids and developed a novel statistical analysis method to identify mixtures of organisms from complex samples hybridized to the array.