Primer articles

  1. What is a hidden Markov model? (Primer 1)
  2. What is the expectation maximization algorithm? (Primer 2)
  3. How does eukaryotic gene prediction work? (Primer 3)
  4. How does DNA sequence motif discovery work? (Primer 4)
  5. Inference in Bayesian networks | A primer on learning in Bayesian networks for computational biology (Primer 5 & 6)
  6. What are decision trees? | What are artificial neural networks? | What is a support vector machine? (Primer 7 & 8 & 9)
  7. MCMC: Does it work? | Understanding the Metropolis-Hastings algorithm | Explaining the Gibbs Sampler (Primer 10 & 11 & 12)

Articles on Deep Learning

  1. Review: Deep learning for computational biology
  2. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (DeepBind)
  3. Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks
  4. Predicting effects of noncoding variants with deep learning–based sequence model (DeepSEA)

Paper presentation

  1. Semisoft clustering of single-cell data (group M+)
  2. Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations (group 2J)
  3. Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome (group JP)
  4. The human splicing code reveals new insights into the genetic determinants of disease
  5. Deep generative modeling for single-cell transcriptomics