220,017 research outputs found
The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain
The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals
Data Mining by Soft Computing Methods for The Coronary Heart Disease Database
For improvement of data mining technology, the advantages and disadvantages on respective data mining methods
should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results
Dissimilarity-based representation for radiomics applications
Radiomics is a term which refers to the analysis of the large amount of
quantitative tumor features extracted from medical images to find useful
predictive, diagnostic or prognostic information. Many recent studies have
proved that radiomics can offer a lot of useful information that physicians
cannot extract from the medical images and can be associated with other
information like gene or protein data. However, most of the classification
studies in radiomics report the use of feature selection methods without
identifying the machine learning challenges behind radiomics. In this paper, we
first show that the radiomics problem should be viewed as an high dimensional,
low sample size, multi view learning problem, then we compare different
solutions proposed in multi view learning for classifying radiomics data. Our
experiments, conducted on several real world multi view datasets, show that the
intermediate integration methods work significantly better than filter and
embedded feature selection methods commonly used in radiomics.Comment: conference, 6 pages, 2 figure
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