Principles and Methods for Data Science

<ol> <ol> <li>Markov chain Monte Carlo methods: Theory and practice</li> <p>David A. Spade</p> <p> <li>An information and statistical analysis pipeline for microbial metagenomic sequencing data</li> <p>Shinji Nakaoka and Keisuke Ohta</p> <p> <li>Machine learning algorithms, applications, and practices in data science</li> <p>Kalidas Yeturu</p> <p> <li>Bayesian model selection for high-dimensional data</li> <p>Naveen Naidu Narisetty</p> <p> <li>Competing risks: Aims and methods</li> <p>Ronald Geskus</p> <p> <li>High-dimensional statistical inference: Theoretical development to data analytics </li> <p>Deepak Nag Ayyala</p> <p> <li>Big data challenges in genomics</li> <p>Hongyan Xu</p> <p> <li>Analysis of microarray gene expression data using information theory and stochastic algorithm</li> <p>Narayan Behera</p> <p> <li>Human life expectancy is computed from an incomplete sets of data: Modeling and analysis</li> <p>Arni S.R. Srinivasa Rao and James R. Carey</p> <p> <li>Support vector machines: A robust prediction method with applications in bioinformatics</li> </ol></ol> <p>Arnout Van Messem</p>