Bioinformatics Commons^™

Gene Co-Expression Networks Analysis Reveal Novel Molecular Endotypes In Alpha-1 Antitrypsin Deficiency, Jen-Hwa Chu, Wenlan Zang

Yale Day of Data

Rationale:Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that predisposes to early onset pulmonary emphysema and airways obstruction. The exact mechanism through which AATD leads to lung disease is incompletely understood.

Objectives: To investigate the effect of AAT genotype and augmentation therapy on bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMC) transcriptome, while examining the link between gene expression profiles, and clinical features of AATD.

Methods: We performed RNA-Seq on RNA extracted from BAL and PBMC on samples obtained from 89 AATD patients enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Differential …

A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan

Yale Day of Data

Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both …

Transcriptomics To Develop Biochemical Network Models In Cyanobacteria, Bridget E. Hegarty, Jordan Peccia, Ratanachat Racharaks

Yale Day of Data

Through targeted genetic manipulations guided by network modeling, we will create a flexible, cyanobacteria-based platform for the production of biofuel-precursors and valuable chemical products. To build gene-metabolite predictive models, we have characterized Synecococcus elongatus sp. UTEX 2973’s (henceforth, UTEX 2973) gene expression and metabolite production under a number of environmental conditions.

Asd Biomarker Detection On Fmri Images: Feature Learning With Data Corruptions By Analyzing Deep Neural Network Classifier Outcomes, Xiaoxiao Li 6984086

Yale Day of Data

Autism spectrum disorder (ASD) is a complex neurological and developmental disorder. It emerges early in life and is generally associated with lifelong disability. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and find more targeted treatment. Previous studies suggested brain activations are abnormal in ASDs, hence functional magnetic resonance imaging (fMRI) has been used to identify ASD. In this work we addressed the problem of interpreting reliable biomarkers in classifying ASD vs. control; therefore, we proposed a 2-step pipeline: 1) classifying ASD and control fMRI images by deep neural network, and …

A Machine Learning Approach To Post-Market Surveillance Of Medical Devices, Jonathan Bates, Shu-Xia Li, Craig Parzynski, Ronald Coifman, Harlan Krumholz, Joseph Ross

Yale Day of Data

Post-market surveillance is a collection of processes and activities used by product manufacturers and regulators, such as the U.S. Food and Drug Administration (FDA) to monitor the safety and effectiveness of medical devices once they are available for use “on the market”. These activities are designed to generate information to identify poorly performing devices and other safety problems, accurately characterize real-world device performance and clinical outcomes, and facilitate the development of new devices, or new uses for existing devices. Typically, a device is monitored by comparing adverse events in the exposed population to a matched unexposed population. This research considers …

K-Mer Analysis On Developmental And Housekeeping Enhancer Peaks, Yunsi Yang, Anurag Sethi, Mark Gerstein

Yale Day of Data

The regulation of gene expression involves interaction between transcriptional enhancers and core promoters. However, the separation between developmental and housekeeping gene regulation remains unknown. Here, we present a method to detect if different core promoters exhibit specificity to certain enhancers within massively parallel assays for enhancer detection. We use k-mers of various length (3-8bp) as sequence features and compare k-mer frequencies between developmental and housekeeping enhancers. This method shows promoter specificity of enhancers in D. melanogaster.

Applying Novel Tree-Based Frameworks To Big Data For Classification Of Heart Failure Patients And Prediction Of Clinical Responses, Yan Zhang, Nicholas Downing, Emily Bucholz, Suganthi Balasubramanian, Shu-Xia Li, Tara Liptak, Harlan Krumholz, Mark Gerstein

Yale Day of Data

Over 5 million Americans suffer from heart failure, a condition with a 5-year survival that eclipses all cancers apart from that of lung cancer. Conventional understanding of heart failure is simplistic: it is viewed as a single syndrome, despite real heterogeneity. In addition, models predicting outcomes focus on dichotomous results, like 30-day readmission. A novel approach to classification of heart failure may improve our ability to target interventions, improve patient experiences, and predict outcomes.

The Healthcare Cost and Utilization Project is a family of administrative claims databases that describes patient demographics, comorbidities, procedures, acute care utilization and outcomes, such as …

Detecting Modules In Multiplex Networks – An Application For Integrating Expression Profiles Across Multiple Species, Koon-Kiu Yan, Daifeng Wang, Joel Rozowsky, Henry Zheng, Baikang Pei, Mark Gerstein

Yale Day of Data

Multiplex network, a set of networks linked through interconnected layers, is a useful mathematical framework for data integration. Here, we present a general method to detect modules in multiplex networks and apply it in a specific biological context: to simultaneously cluster the genome-wide expression profiles of C. elegans and D. melanogaster generated by the ENOCDE and modENCODE consortia. The method revealed modules that are fundamentally cross-species and can either be conserved or species-specific. In general, the method could be applied in various contexts like the integration of different social networks.