Bioinformatics Commons^™

Developing Graphic Libraries To Accompany The Craniofacial Human Ontology, Melissa D. Clarkson

Institute for Biomedical Informatics Faculty Publications

I describe the development of two graphic libraries to accompany parts of the Craniofacial Human Ontology. One library depicts phenotypes of cleft lip. The other represents development of the human head between 4 and 8 weeks of gestation.

Does The Foundational Model Of Anatomy Ontology Provide A Knowledge Base For Learning And Assessment In Anatomy Education?, Melissa D. Clarkson, Mark E. Whipple

Institute for Biomedical Informatics Faculty Publications

Throughout the development of the Foundational Model of Anatomy (FMA) ontology, one of the use cases put forth has been anatomy education. In this work, we examine which types of knowledge taught to anatomy students can be supported by the FMA knowledge base. We first categorize types of anatomical knowledge, then express these patterns in the form “Given ____, state ____”. Each of the 33 patterns was evaluated for whether this type of knowledge is compatible with the modeling and scope of the FMA.

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …

Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.

Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …

Immune Checkpoint Inhibition And The Prevalence Of Autoimmune Disorders Among Patients With Lung And Renal Cancer, Sherif M. El-Refai

Pharmaceutical Sciences Faculty Publications

PURPOSE: Immune checkpoint inhibition reactivates the immune response against cancer cells in multiple tissue types and has been shown to induce durable responses. However, for patients with autoimmune disorders, their conditions can worsen with this reactivation. We sought to identify, among patients with lung and renal cancer, how many harbor a comorbid autoimmune condition and may be at risk of worsening their condition while on immune checkpoint inhibitors such as nivolumab and pembrolizumab.

METHODS: An administrative health care claims database, Truven MarketScan, was used to identify patients diagnosed with lung and renal cancer from 2010 to 2013. We assessed patients …

Cross-Talk Between Clinical And Host-Response Parameters Of Periodontitis In Smokers, Radha Nagarajan, Craig S. Miller, Dolph R. Dawson Iii, Mohanad Al-Sabbagh, Jeffrey L. Ebersole

Institute for Biomedical Informatics Faculty Publications

Background and Objective

Periodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host‐response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens …

A Fast And Efficient Python Library For Interfacing With The Biological Magnetic Resonance Data Bank, Andrey Smelter, Morgan Astra, Hunter N. B. Moseley

Center for Environmental and Systems Biochemistry Faculty Publications

Background: The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis.

Results:To aid in the use of this public resource, we have developed a package called …

Roadmap To A Comprehensive Clinical Data Warehouse For Precision Medicine Applications In Oncology, David J. Foran, Wenjin Chen, Huiqi Chu, Evita Sadimin, Doreen Loh, Gregory Riedlinger, Lauri A. Goodell, Shridar Ganesan, Kim Hirshfield, Lorna Rodriguez, Robert S. Dipaola

Internal Medicine Faculty Publications

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic …

Phylotoast: Bioinformatics Tools For Species-Level Analysis And Visualization Of Complex Microbial Datasets, Shareef M. Dabdoub, Megan L. Fellows, Akshay D. Paropkari, Matthew R. Mason, Sarandeep S. Huja, Alexandra A. Tsigarida, Purnima S. Kumar

Oral Health Science Faculty Publications

The 16S rRNA gene is widely used for taxonomic profiling of microbial ecosystems; and recent advances in sequencing chemistry have allowed extremely large numbers of sequences to be generated from minimal amounts of biological samples. Analysis speed and resolution of data to species-level taxa are two important factors in large-scale explorations of complex microbiomes using 16S sequencing. We present here new software, Phylogenetic Tools for Analysis of Species-level Taxa (PhyloToAST), that completely integrates with the QIIME pipeline to improve analysis speed, reduce primer bias (requiring two sequencing primers), enhance species-level analysis, and add new visualization tools. The code …

Exploration Of The Srx-Prx Axis As A Small-Molecule Target, Murli Mishra

Theses and Dissertations--Toxicology and Cancer Biology

Lung cancer is a leading cause of cancer-related mortality irrespective of gender. The Sulfiredoxin (Srx) and Peroxiredoxin (Prx) are a group of thiol-based antioxidant proteins that plays an essential role in non-small cell lung cancer. Understanding the molecular characteristics of the Srx-Prx interaction may help design the strategies for future development of therapeutic tools. Based on existing literature and preliminary data from our lab, we hypothesized that the Srx plays a critical role in lung carcinogenesis and targeting the Srx-Prx axis or Srx alone may facilitate future development of targeted therapeutics for prevention and treatment of lung cancer. First, …

Examination Of The Snsag Surface Antigen Gene Family In Sarcocystis Neurona, Ablesh Gautam

Theses and Dissertations--Veterinary Science

Sarcocystis neurona is a protozoan parasite that causes the serious neurologic disease equine protozoal myeloencephalitis (EPM). The life cycle of S. neurona progresses through multiple developmental stages that differ morphologically and molecularly. The S. neurona merozoite surface is covered by multiple related proteins, which are orthologous to the surface antigen (SAG) gene family of Toxoplasma gondii. The SAG surface antigens in T. gondii and another related parasite Neospora caninum are life cycle stage-specific and seem necessary for parasite transmission and persistence of infection. The present research was conducted to explore the gene family of SnSAGs in S. …

Impact Of Noise On Molecular Network Inference, Radhakrishnan Nagarajan, Marco Scutari

Biostatistics Faculty Publications

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement …

Lassa: Emotion Detection Via Information Fusion, Ning Yu, Sandra Kübler, Joshua Herring, Yu-Yin Hsu, Ross Israel, Charese Smiley

Information Science Faculty Publications

Due to the complexity of emotions in suicide notes and the subtle nature of sentiments, this study proposes a fusion approach to tackle the challenge of sentiment classification in suicide notes: leveraging WordNet-based lexicons, manually created rules, character-based n-grams, and other linguistic features. Although our results are not satisfying, some valuable lessons are learned and promising future directions are identified.