Bridging the gap: A library-based collaboration to enhance data skills for clinical researchers.

Introduction: Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system-wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end-to-end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library-based services for the campus.

Methods: The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction.

Risk Adjusting Health Care Provider Collaboration Networks.

Objectives: The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives.

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions.

A disease similarity matrix based on the uniqueness of shared genes.

Background: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing.

Node Attribute-enhanced Community Detection in Complex Networks.

Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes.

Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach.

Background: The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment.

Methods And Results: We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine's Enterprise Data Warehouse.

An Outcome-Weighted Network Model for Characterizing Collaboration.

Shared patient encounters form the basis of collaborative relationships, which are crucial to the success of complex and interdisciplinary teamwork in healthcare. Quantifying the strength of these relationships using shared risk-adjusted patient outcomes provides insight into interactions that occur between healthcare providers. We developed the Shared Positive Outcome Ratio (SPOR), a novel parameter that quantifies the concentration of positive outcomes between a pair of healthcare providers over a set of shared patient encounters.

Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification.

Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.

Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit.

Identification of cancer-related genes and motifs in the human gene regulatory network.

The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes.

Network-based prediction and knowledge mining of disease genes.

Background: In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules.

Visualizing collaborative electronic health record usage for hospitalized patients with heart failure.

Objective: To visualize and describe collaborative electronic health record (EHR) usage for hospitalized patients with heart failure.

Materials And Methods: We identified records of patients with heart failure and all associated healthcare provider record usage through queries of the Northwestern Medicine Enterprise Data Warehouse. We constructed a network by equating access and updates of a patient's EHR to a provider-patient interaction.

A new exhaustive method and strategy for finding motifs in ChIP-enriched regions.

ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (l, d) motifs (l ≤ 10, d ≤ 2) contained in ChIP-enriched regions.

A fast weak motif-finding algorithm based on community detection in graphs.

Background: Identification of transcription factor binding sites (also called 'motif discovery') in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites. State-of-the-art algorithms have their own constraints (e.

Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets.

Through combinatorial regulation, regulators partner with each other to control common targets and this allows a small number of regulators to govern many targets. One interesting question is that given this combinatorial regulation, how does the number of regulators scale with the number of targets? Here, we address this question by building and analyzing co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We carry out analyses across five diverse species: Escherichia coli to human.

NAPS: a residue-level nucleic acid-binding prediction server.

Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins.

Mining knowledge for the methylation status of CpG islands using alternating decision trees.

CpG island (CpGI) methylation is an epigenetic modification that occurs in eukaryotes and is based on the addition of a methyl group to the number 5 carbon of the pyrimidine ring of cytosine. When methylation of a CpGI occurs, the associated gene (if any) is not expressed [1]. Aberrant methylation is thought to be a causative agent in disease [2] and drug sensitivity [3], [4].

Prediction of specific protein-DNA recognition by knowledge-based two-body and three-body interaction potentials.

Gene regulation requires specific protein-DNA interactions. Detecting the short and variable DNA sequences in gene promoter regions to which transcription factors (TF) bind is a difficult challenge in bioinformatics. Here we have developed two-body and three-body interaction potentials that are able to assess protein-DNA interaction and achieve a higher level of specificity in the recognition of TF-binding sites.

Learning to translate sequence and structure to function: identifying DNA binding and membrane binding proteins.

A protein's function depends in a large part on interactions with other molecules. With an increasing number of protein structures becoming available every year, a corresponding structural annotation approach identifying such interactions grows more expedient. At the same time, machine learning has gained popularity in bioinformatics providing robust annotation of genes and proteins without sequence homology.

Identification of the iron-responsive genes of Neisseria gonorrhoeae by microarray analysis in defined medium.

To ensure survival, most bacteria must acquire iron, a resource that is sequestered by mammalian hosts. Pathogenic bacteria have therefore evolved intricate systems to sense iron limitation and regulate gene expression appropriately. We used a pan-Neisseria microarray to examine genes regulated in Neisseria gonorrhoeae in response to iron availability in defined medium.

Genomic sequence of an otitis media isolate of nontypeable Haemophilus influenzae: comparative study with H. influenzae serotype d, strain KW20.

In 1995, the Institute for Genomic Research completed the genome sequence of a rough derivative of Haemophilus influenzae serotype d, strain KW20. Although extremely useful in understanding the basic biology of H. influenzae, these data have not provided significant insight into disease caused by nontypeable H.

Genome sequence of Streptococcus mutans UA159, a cariogenic dental pathogen.

Streptococcus mutans is the leading cause of dental caries (tooth decay) worldwide and is considered to be the most cariogenic of all of the oral streptococci. The genome of S. mutans UA159, a serotype c strain, has been completely sequenced and is composed of 2,030,936 base pairs.