Introduction

• A Primer on Molecular Biology   - Alexander Zien - 2004
• Electronic Statistics Textbook  - StatSoft, Inc.  - 2004
• Mini tutorial on the Singular Value Decomposition - Dean - 1995
  

Clustering

• Cluster Analysis for Gene Expression Data: A Survey - Daxin Jiang, Aidong Zhang - 2002
• Normalized cuts and image segmentation - Shi et al. - 1998
  
• Multiclass Spectral Clustering  - Stella Yu and Jianbo Shi - 2003
  
• Kernel k-means, Spectral Clustering and Normalized Cuts  Dhillon et al. - 2004

Bi-clustering

• Biclustering of expression data - Cheng, Church - 2000
• Minimum sum squared residue co-clustering of gene expression data- Cho, Dhillon et al. - 2004

Classification

• A Tutorial on Support Vector Machines for Pattern Recognition - Burges - 1998
• Support Vector Machine Applications in Computational Biology - William S. Noble - 2004
• Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems - Ye - 2005
• Classification of Gene Microarrays by Penalized Logistic Regression - Zhu and Hastie - 2004
  

Semi-supervised learning

• Regularization and Semi-supervised Learning on Large Graphs - Belkin et al. - 2005
• Semi-supervised Learning on Riemannian Manifolds Belkin and Niyog - 2005
• Learning with Local and Global Consistency - Zhou et al. - 2003
• Semi-Supervised Learning Using Gaussian Fields and Harmonic functions - Zhu, Ghahramani et al. - 2003

Feature reduction

• Geometric Methods for Feature Extraction and Dimensional Reduction - Burges- 2005
• Extraction of Correlated Gene Clusters from Multiple Genomic Data by Generalized Kernel Canonical Correlation Analysis - Yamanishi et al. - 2003
• Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA - Vert - 2003
  

Manifold learning

• A global geometric framework for nonlinear dimensionality reduction  - Tenenbaum et al. - 2000
• Nonlinear Dimensionality Reduction by Locally Linear Embedding - Roweis and Saul - 2000
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation - Belkin and Niyog - 2003
  

Kernel learning

• A Primer on Kernel Methods - Jean-Philippe Vert, Koji Tsuda and Bernhard Schölkopf - 2004
  
• Inexact Matching String Kernels for Protein Classification- Christina Leslie, Rui Kuang and Eleazar Eskin - 2004
• Kernels for Graphs - Hisashi Kashima, Koji Tsuda and Akihiro Inokuchi - 2004
  
• Kernel-Based Integration of Genomic Data Using Semidefinite Programming - Gert R. G. Lanckriet, Nello Cristianini, Michael I. Jordan and William S. Noble - 2004
  

Bagging and boosting

• Experiments with a New Boosting Algorithm - Freund and Schapire - 1996
• Bagging Predictors - Breiman - 1996
• Additive Logistic Regression: a Statistical View of Boosting - Friedman et al. - 1998
  
• Ensemble Methods in Machine Learning  - Dietterich - 2000