Biological Data Mining

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Dereeper, A. Plant Mol.

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Leroy, T. Yu, Q. Plant J. Llorens, C. Nucleic Acids Res. Wicker, T.

Journal news

Witte, C. Kalendar, R. Genetics , — CrossRef. Tanskanen, J.

Publisher Description

Gene , — CrossRef. Quesneville, H.

PLoS Comput. Price, A. Bioinformatics 21 , — CrossRef.

  • Bioinformatics and Biological Data Mining Symposium.
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Ellinghaus, D. BMC Bioinform. McCarthy, E. Bioinformatics 19 , — CrossRef.

Xu, Z. Disdero, E. DNA 8 , 5 CrossRef.

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  • Program Committee:?
  • Editors-in-Chief.
  • Biological Data Mining And Its Applications In Healthcare.
  • Littl3field?

Zeng, F. Plant Sci. Hoede, C.

Guest editorial

Steinbiss, S. Du, J. The goal of this workshop is to encourage KDD researchers to take on the numerous challenges that Bioinformatics offers. This field focuses on the use of data mining and machine learning approaches for the analysis of the large amount of heterogeneous complex biological and medical data being generated. The direction of deep learning methods is particularly encouraged. The goal here is to build accurate predictive or descriptive models from these data enabling novel discoveries in basic biology and medicine.

We encourage papers that propose novel data mining techniques for areas including but not limited to :. Development of deep learning methods for biological and clinical data. Building predictive models for complex phenotypes from large-scale biological data. Discovering biological networks and pathways underlying biological processes and diseases. Analysis, discovery of biomarkers and mutations, and disease risk assessment.

Discovery of genotype-phenotype associations. Novel methods and frameworks for mining and integrating big biological data. Comparative genomics.

FDP on Biological Data Mining and Analytics by (CS) - Sep

Metagenome analysis using sequencing data. RNA-seq and microarray-based gene expression analysis. Next, the benchmark data is applied to test two additional network discovery methods: K2 and BDeu, used for Bayesian networks. Our approach outperforms both Bayesian methods.

University of Texas Biological Data Mining Research Group

The results demonstrate similar improvements in false positives and accuracy. Analyses reveal visible differences between the sexes, as well as between the transgenic and conventional pig lines. Also, strong associations are evident among the minerals and the fatty acids, regardless of the sex and pig line. The detected association patterns are observed to be stronger among males and conventional pigs.

Project Funding