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BBH 2014 : The 2nd International Workshop of BigData in Bioinformatics and Healthcare Informatics

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Link: http://bbh14.analyzegenomes.com/
 
When Oct 27, 2014 - Oct 27, 2014
Where Washington D.C., USA
Submission Deadline Aug 4, 2014
Notification Due Sep 15, 2014
Final Version Due Sep 28, 2014
Categories    big data   bioinformatics   healthcare   databases
 

Call For Papers


CALL FOR PAPERS
==============

*** The 2nd International Workshop on Big Data in Bioinformatics and Healthcare Informatics (BBH14) ***

in conjunction with
The IEEE International Conference on BigData (IEEE BigData 2014)

Web: http://bbh14.analyzegenomes.com
Date: Oct 27, 2014
Venue: Hyatt Regency Bethesda, One Bethesda Metro Center (7400 Wisconsin Ave), Bethesda, Maryland, 20814, United States

BBH is the leading forum for research, work-in-progress, and applications addressing big data challenges. We are calling for papers presenting concepts, infrastructure, and analytical tools that integrate data from heterogeneous data sources to provide new insights for researchers and industries.

Sincerely,

Your program chairs

Matthieu-P. Schapranow, Menglin ‘Mornin’ Feng, Luke Huan, Vinay Pai, Ankur Teredesai, Shipeng Yu


IMPORTANT DATES
===============

Paper submission: Aug 4, 2014
Notification of acceptance: Sep 15, 2014
Submission of camera-ready papers: Sep 28, 2014

TOPICS OF INTEREST
==================

We welcome submissions covering various aspects of big data processing and analysis in “Bioinformatics” and “Healthcare Informatics”. Areas of interest include but are not limited to computer science, in-memory technology, computational science, biological, biomedical, pharmaceutical, nursing, clinical care, dentistry, and public health.

BIOINFORMATICS AND BIOMEDICAL INFORMATICS
=========================================

Next-generation sequencing (NGS) data storage and analysis
Large scale biological network construction and learning
Population-based bioinformatics
Genome structural change detection
Large-scale bio-image and medical-image analysis
Big data in molecular simulation and protein structure prediction
Big data in systems biology
Big data in precision medicine and stratified medicine
Big data in drug discovery, development, and post-market surveillance
Big data in semantics and bio-text mining

HEALTHCARE SYSTEMS
==================

Real-time aspects of healthcare data infrastructure
Security and privacy for clinical data in big data infrastructures
Health IT implementations and demonstrations
Case studies for healthcare analysis in distributed environments
Benchmarking of big data infrastructure in healthcare
Novel data analysis algorithms that enable integrated discovery of knowledge from structured and unstructured Electronic Medical Records (EMR)
Analysis and visualizing for summarizing large patient data in EMRs
Novel algorithms and applications dealing with noisy, incomplete, but large EMR data
Integrating genomic data in today’s medicine to improve human health
Data science and modeling for health analysis
Advances in new storage models for data variety (records, images, Magnetic Resonance Imaging (MRI), scans) for hospitals
Big data challenges in accountable care settings
Extracting meaning from multi-structured big data in real time to improve outcome
Combining information from imaging (RIS, PACS), Electronic Health Records (EHR), laboratories, genomics to give coherent diagnosis and treatment
Leveraging social networks for data aggregation
Smart visualizations for big data streams
Analysis of big data from home monitoring devices
Design patterns and anti-patterns for development of solutions for big data

ANALYSIS OF BIG MEDICAL DATA
============================

Real-time analysis of big medical data in the course of precision medicine
Analysis of longitudinal and time-series data to discover new correlations
Co-registration of patient data acquired over several time-points in their life
Identification of important metadata that has to be tracked over a longitudinal duration
Software platforms for enabling easy access to the patient’s medical and clinical history
Gap-handling in history-taking
Quality improvement and noise-handling on longitudinal data
Missing functionality in current clinical decision support systems using longitudinal data

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