Histone Signatures Predict Therapeutic Efficacy in Breast Cancer.

Regulatory abnormalities caused by chromatin modifications are being increasingly recognized as contributors to cancer. While many molecularly targeted drugs have the potential to revert these modifications, their precise mechanism of action in cellular reprogramming is not known. To address this, we introduce an integrated phosphoprotein-histone-drug network (iPhDNet) approach to generate "global chromatin fingerprints of histone signatures.

Diagnostic Prediction of Von Willebrand Disease using Multiple Bleeding Phenomics Datasets.

The rigorous assessment of bleeding symptoms is a key component in establishing a diagnosis in patients suspected of having von Willebrand disease (VWD) and other inherited bleeding disorders. Multiple bleeding questionnaires have been developed and validated to capture bleeding history phenotypes for assessing patients with bleeding disorders. In this study we developed a prediction model based on Naïve Bayes decision tree classifier by analyzing various phenotypic attributes derived from multiple bleeding questionnaires.

Flt3L dependence helps define an uncharacterized subset of murine cutaneous dendritic cells.

Skin-derived dendritic cells (DCs) are potent antigen-presenting cells with critical roles in both adaptive immunity and tolerance to self. Skin DCs carry antigens and constitutively migrate to the skin-draining lymph nodes (LNs). In mice, Langerin-CD11b- dermal DCs are a low-frequency, heterogeneous, migratory DC subset that traffics to LNs (Langerin-CD11b- migDCs).

Ontology-based federated data access to human studies information.

Human studies are one of the most valuable sources of knowledge in biomedical research, but data about their design and results are currently widely dispersed in siloed systems. Federation of these data is needed to facilitate large-scale data analysis to realize the goals of evidence-based medicine. The Human Studies Database project has developed an informatics infrastructure for federated query of human studies databases, using a generalizable approach to ontology-based data access.

The human studies database project: federating human studies design data using the ontology of clinical research.

Human studies, encompassing interventional and observational studies, are the most important source of evidence for advancing our understanding of health, disease, and treatment options. To promote discovery, the design and results of these studies should be made machine-readable for large-scale data mining, synthesis, and re-analysis. The Human Studies Database Project aims to define and implement an informatics infrastructure for institutions to share the design of their human studies.

Creating an ontology-based human phenotyping system: The Rockefeller University bleeding history experience.

The lack of standardized methods for human phenotyping is a major obstacle in translational science. We have developed a bleeding history phenotyping system comprising an ontology, a questionnaire, a Web-based phenotype recording instrument (PRI), and a database. The ontology facilitates transparency, collaboration, aggregation of data, and data analysis.

Development and evaluation of a study design typology for human research.

A systematic classification of study designs would be useful for researchers, systematic reviewers, readers, and research administrators, among others. As part of the Human Studies Database Project, we developed the Study Design Typology to standardize the classification of study designs in human research. We then performed a multiple observer masked evaluation of active research protocols in four institutions according to a standardized protocol.

Semi automatic indexing of PostScript files using Medical Text Indexer in medical education.

At Albert Einstein College of Medicine a large part of online lecture materials contain PostScript files. As the collection grows it becomes essential to create a digital library to have easy access to relevant sections of the lecture material that is full-text indexed; to create this index it is necessary to extract all the text from the document files that constitute the originals of the lectures. In this study we present a semi automatic indexing method using robust technique for extracting text from PostScript files and National Library of Medicine's Medical Text Indexer (MTI) program for indexing the text.

Automatic learning of the morphology of medical language using information compression.

Conversion of free-text strings in a natural language to a standard representation (codes) is an important reoccurring problem in biomedical informatics. Determining the content of a string involves identifying its meaningful constituents (morphemes). One current method of identifying these constituents is to look them up in a preexisting table (lexicon).