I will cover here only some special algorithms which can be used for metabolomics or structure elucidation of small molecules. This includes feature selection, classification and regression and some machine learning algorithms and genetic algorithms.

For machine learning purposes we usually apply diverse feature selection methods (sometimes based on the following 15 methods or genetic feature selection or PLS feature selection) and we use classification and regression methods from the following table (sometimes together with meta-learning concepts like bagging, boosting, voting, stacking).

Generalized Linear Models (GLM)

Nonlinear models

Tree models

Standard Classification Trees (CART)
Standard General Chi-square Automatic Interaction Detector (CHAID)
Exhaustive CHAID
Boosting classification treesM5 regression trees

Neural Networks

Multilayer Perceptron
neural network (MLP)
Radial Basis Function neural network (RBF)

Machine Learning

Support Vector Machines (SVM)
Naive Bayes classifier
k-Nearest Neighbors (KNN)

Additional resources:

Book - HANDBOOK OF STATISTICAL ANALYSIS AND DATA MINING APPLICATIONS
Hardbound, 864 pages, publication date: MAY-2009 (with DVD and online tutorials
ISBN-13: 978-0-12-374765-5
www.elsevier.com/wps/product/cws_home/717661

Find it in a library near you (OCLC Worldcat) dont forget to update your ZIP
http://www.worldcat.org/oclc/316327105