Characteristics of Successful Health Alliance Strategies: Evidence From Rural Healthcare Experiences.

EXECUTIVE SUMMARY: The formation of regional strategic alliances continues to be a well-evidenced response to a varying array of market forces that are challenging the ability of healthcare institutions to realize their missions. Organizations that serve rural communities especially feel pressure to initiate the formation of these collaborative arrangements.In response to concerns of Pennsylvania legislators regarding the impact of these alliances on rural healthcare entities, the Center for Rural Pennsylvania funded a study of outcomes of regional strategic alliances involving rural healthcare institutions. Although the research focused on outcomes, the data also revealed organizational characteristics and patterns of decisions and actions that separated rural healthcare institutions with greater alliance outcome success from their peers serving other rural communities. Strategic leadership and interorganizational management expertise serve as the foundation for decisions and actions beginning before an active search for an alliance and culminating with the achievement of alliance goals. Commitments to collaborative leadership, purposeful partnership, coordination, and progress thematically represent the series of critical decisions and actions collectively required to achieve strategic alliance success. The case of the Laurel Health System illustrates these commitments.Although the findings are based on an intensive review of regional strategic alliances involving rural healthcare institutions, the lessons presented here are transferable to community healthcare organizations regardless of location.

KNGP: A network-based gene prioritization algorithm that incorporates multiple sources of knowledge.

BACKGROUND: Candidate gene prioritization is the process of identifying and ranking new genes as potential candidates of being associated with a disease or phenotype. Integrating multiple sources of biological knowledge for gene prioritization can improve performance. RESULTS: We developed a novel network-based gene prioritization algorithm called Knowledge Network Gene Prioritization (KNGP) that can incorporate node weights in addition to the usually used link weights. The online Web implementation of KNGP can handle small input files while the downloadable R software package can handle larger input files. We also provide several files of coded biological knowledge that can be used by KNGP.

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links.

BACKGROUND: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. RESULTS: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. CONCLUSIONS: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.

Using a novel resource to decrease proteomic biomarker identification time.

The Empirical Proteomic Ontology Knowledge Base (EPO-KB) is an online database that represents current knowledge of biomarkers and contains associations between mass-to-charge (m/z) ratios of mass-spectrometry peaks to proteins. Such a database is a useful tool for identifying putative proteins associated with a m/z ratio. At present, EPO-KB contains data that have been extracted from 120 published research papers. It has been used in successful identification of a protein associated with a biomarker.

Wrapper consistency analysis: a measure of consistency in a wrapper setting.

Typically, in high-throughput data, the number of features is often substantially greater than the number of samples. One approach to this statistical challenge is to perform feature selection and usually, only predictive accuracy is used to perform feature selection. We present a new feature selection method called Wrapper Consistency Analysis that strives to optimize both the predictive accuracy as well as a measure called overlap (or consistency). We believe this analysis provides additional information about the goodness of a selected set of features.

EPO-KB: a searchable knowledge base of biomarker to protein links.

UNLABELLED: The knowledge base EPO-KB (Empirical Proteomic Ontology Knowledge Base) is based on an OWL ontology that represents current knowledge linking mass-to-charge (m/z) ratios to proteins on multiple platforms including Matrix Assisted Laser/Desorption Ionization (MALDI) and Surface Enhanced Laser/Desorption Ionization (SELDI)--Time of Flight (TOF). At present, it contains information on m/z ratio to protein links that were extracted from 120 published research papers. It has a web interface that allows researchers to query and retrieve putative proteins that correspond to a user-specified m/z ratio. EPO-KB also allows automated entry of additional m/z ratio to protein links and is expandable to the addition of gene to protein and protein to disease links. AVAILABILITY: http://www.dbmi.pitt.edu/EPO-KB