Challenges of Pattern Recognition in Biomedical Data

Motivation

The analysis of big biomedical data is often presented with various challenges associated with the heterogeneity, multidimensionality, noisiness and incompleteness of the data itself, but also related to the computational resources required to complete the analysis. The data-intensive nature of computational genetics problem sets in the biomedical informatics field warrants the development and use of massive computer infrastructure and advanced software tools and platforms, including but not limited to the use of cloud computing. In this session, we will address innovative ways to identify and overcome challenges associated with the quality of various types of biomedical data, including Electronic Health Records, medical imaging etc. Additionally, we will focus on issues related to the optimization of tool development for large-scale datasets (keeping in mind issues like computing time and storage, the need for parallelization), as well as challenges associated with the cost in both time and resources of pattern recognition computational methods. Lastly, we will also address the challenges arising from trying to integrate biomedical data from various sources (including, but not limited to, one or across more species, use of raw data, or summary level statistics) to identify patterns in these multi-omic datasets.

Mauna Kea Mauna Kea Mauna Kea

Session Topics

Topics within the scope of this session include:

Development and use of novel deep learning and machine learning approaches to address challenges in pattern recognition such as
  • Electronic Health Record (EHR)
  • Medical Imaging
  • Natural language processing and others
Addressing data analysis challenges with variety of differrnt imperfect dataset such as
  • Sparse datasets
  • Noisy data
  • Integration of multidimensional data
Challenges of computational resource requirements for large-scale and high-dimensionality data analysis.
  • GPU
  • Hadoop
  • Cloud computing
  • Databases
Data mining approaches to promote the use of metadata as vital methods in integrating data from various sources and predicting disease outcomes.
Visualization of patterns in big data and novel approaches to enhance its reproducibility
Other related topics.

Session Organizers

Anna Basile

The Pennsylvania State University
azo121@psu.edu

Submission Information

Paper Submission Deadline: August 1 August 8, 2017 at noon PT
Notification of Acceptance: September 15, 2017
Revised Papers Due: October 2, 2017
Poster/Abstract Submission Deadline: November 13, 2017
Conference Date: January 3 - 7, 2018

Papers must be submitted to the PSB paper management system. Please note that the submitted papers are reviewed and accepted on a competitive basis. At least three reviewers will be assigned to each submitted manuscript.

The accepted file formats are: postscript (*.ps) and Adobe Acrobat (*.pdf). Attached files should be named with the last name of the first author (e.g. altman.ps or altman.pdf). Hardcopy submissions or unprocessed TEX or LATEX files or electronic submissions not submitted through the paper management system will be rejected without review.

Each paper must be accompanied by a cover letter. The cover letter should be the first page of your paper submission. The cover letter must state the following:

  • The email address of the corresponding author
  • The specific PSB session that should review the paper or abstract
  • The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere
  • All co-authors concur with the contents of the paper

Submitted papers are limited to twelve (12) pages (not including the cover letter) in our publication format. Please format your paper according to the instructions. If figures can not be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit.