Drug Repositioning for Subgroup discovery and Precision Medicine Implementation

Drug discovery is a high-cost, time-consuming, and labor-intensive process. With the declined approval rate for new drugs by the FDA, developing drug repositioning frameworks becomes crucial for improving patient care. Drug repositioning, known as old drugs for new uses, is an effective strategy to find new indications for existing drugs and is highly efficient, low-cost, and less risk. The proposed work is a network-based computational approach for subgroup cohort drug repositioning. Precision medicine is getting more attention to be applied in today’s healthcare system toward a more patient-centered system rather than a disease-based one. The phenotypic and genotypic variation among…

Hierarchical agglomerative clustering of eusocial bee proteins

The assembly and annotation of the European Honey bee (Apis mellifera) genome has predicted more than 15,000 protein-coding genes and became the foundation for studies of nature and evolution of eusociality. Since then, other eusocial bee genomes have been sequenced, providing an excellent opportunity to seek additional insight into unique traits of eusocial bees at the genomic and proteomic level. We wish to build a non-redundant organization of protein sequences by applying an unsupervised hierarchical protein-clustering method to the protein sequences of 4 advanced eusocial honey bees and 2 primitively eusocial bumblebees. The clustering method will group proteins into clusters…

Mutational Forks: Inferring Pathway Deregulation Based on Patient-Specific Genomics Profiles

The precise mechanism behind treatment resistance in cancer is still not fully understood. Despite advances in precision oncology, there is a lack of the tools that help to understand a mechanistic picture of treatment resistance in cancer patients. Existing enrichment methods heavily rely on quantitative data and limited to analysis of differentially expressed genes, ignoring crucial players that might be involved in this process. In order to tackle treatment resistance, the identification of deregulated flow of signal transduction is critical. Here, we introduce a bioinformatics framework that is capable of inferring deregulated flow of signal transduction given evidence-based knowledge about…

LARGE-SCALE SOYBEAN GENOME-WIDE VARIATION WORKFLOW AND ASSOCIATION ANALYSIS USING DEEP LEARNING

With the advances in next-generation sequencing technology and significant reduction in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations, and apply the knowledge towards improvements in traits. To facilitate large-scale NGS resequencing data analysis of genomic variations efficiently, we developed a systematic solution using high-performance computing environment, cloud data storage resources and graphics processing unit computing with cutting-edge deep learning approach. The solution contains an integrated and optimized variant calling workflow called ‘PGen’, a quantitative phenotype prediction model using convolutional neural network and an algorithm to study genome-wide association…

Extension of the informatics framework for the structurization of free text diagnostic reports

In this seminar presentation, we will briefly discuss our recent project to extend our informatics pipeline for the structurization of free-text diagnostic reports with an information theory-based approach. We will focus on the steps of this approach that are for quantifying and measuring information in free text diagnostic reports. This is a work in progress.

Analysis of Curated Toll-Like-Receptor Co-expression Network Reveals associations among RNAseq Expression and Immune system modulation in patients with Colorectal Cancer

This project is concerned with improving immune system capabilities in the context of immunotherapy for colorectal cancer treatments.  Immunotherapies called checkpoint inhibitors are monoclonal antibody treatments that have been proven to be effective in small percentages of sample populations, especially upon a combination of treatments (e.g. PD-1 with CTLA-4).  However, the effectiveness of the treatment response often accompanied by contraindications of autoimmunity.   Allocation of our efforts towards computational modeling of communication networks specific to TLR signaling affords a more intimate study of innate immunity, and the corresponding responses generated, as they pertain to the Tumor Microenvironment.  Targeting the mechanisms…

Addressing Challenges of Deep Learning for Post-Disaster Damage Classification

The effectiveness of natural disaster response functions has increasingly relied on information extracted from remotely-sensed imagery – particularly for the time-critical and often large-scale task of assessing post-disaster damage to the built environment. Despite a recent push to operationalize deep learning (DL) technologies to assist this task, several overlooked and unaddressed challenges limit DL’s applicability. First, very little DL research in the remote sensing domain utilizes the geographic variation in objects of interest or ancillary spatial data layers as features which could help the algorithms learn. Second, there is a scarcity of labelled training data depicting the effects of common…

The Role of Informatics in the Implementation of Population Precision Health

Abstract: Precision health is an emerging concept in healthcare. Coupled with the movement towards Learning Healthcare Systems, there is an opportunity to transform how care and prevention are imagined. To realize this vision requires collection and analysis of large amounts of disparate data, development of knowledge from the data, and seamless ways to deliver the knowledge back to clinicians and, increasingly, patients. Informatics is expected to play a key role in precision health. In this presentation, Dr. Williams will provide some background and philosophy on different terms in current use, will introduce Geisinger’s MyCode Community Health Initiative—the largest implemented population…

Using Geospatial Context: Facilitating Geospatial Analysis in Research

This presentation will briefly review the Geospatial Context Big Table status and its current evolution.  Then the presentation turns to two projects that have used or are using geospatial context in the analysis and visualization of their results.  The first project is a zoonotic disease study originally undertaken as a class project and lab rotation.  The second is a study of Thyroid Cancer and possible exposome radiation influences and population stability metrics.  In each case focus will be on developing focus and assembling data and possible surrogates, compiling into units of analysis, and the modeling of those relationships.