Data Mining in Structural Biology

 Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records, unusual records , and dependencies . Multi-relational data mining tools have been applied to a variety of biological tasks. Biological data bases provide a major challenge for multi-relational data mining. Already, data mining researches should be more ambitious in applying multi-relational algorithms to larger and more diverse databases.

Related Conferences

9th International Conference on Structural Biology September 18-20, 2017 Zurich, Switzerland;2nd International Conference on Biochemistry September 28-29, 2017 Dubai, UAE; 10th International Conference and Exhibition on Metabolomics October 19-20, 2017 Baltimore, USA; International Conference on Next Generation Sequencing and Biostatistics October 23-24, 2018 Dubai; 3rd International Conference on Transcriptomics October 30 - November 01, 2017 Bangkok, Thailand; 9th International Conference and Expo on Proteomics November 13-15, 2017 Paris, France; 3rd International Conference on Genetic and Protein Engineering November 08-09, 2017 Las Vegas, USA; 9th International Conference on Bioinformatics November 13-14, 2017 Paris, France.

  • Biological data mining
  • Advances in data mining
  • Data mining in crystallography
  • Data mining in bio-informatics
  • Data mining for biomarker discovery

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