• ​​​​Preface.- Novel Biclustering Methods for Re-Ordering DataMatrices (P.A. DiMaggio Jr., A. Subramani, C.A. Floudas).- Clustering TimeSeries Data with Distance Matrices (O. Şeref, W.A. Chaovalitwongse).- Mathematical Models of SupervisedLearning and Application to Medical Diagnosis (R. De Asmundis, M.R.Guarracino).- Predictive Model for Early Detection of Mild Cognitive Impairmentand Alzheimer’s Disease (E.K. Lee, T.-L. Wu, F. Goldstein, A. Levey).-Strategies for Bias Reduction in Estimation of Marginal Means with Data Missingat Random (B. Chen, R.J. Cook).- Cardiovascular Informatics: A Perspective onPromises and Challenges of IVUS Data Analysis (I.A. Kakadiaris, E.G.M. Ruiz).-An Introduction to the Analysis of Functional Magnetic Resonance Imaging Data(G. Gazzola, C.-A. Chou, M.K. Jeong, W.A. Chaovalitwongse).- SensoryNeuroprostheses: From Signal Processing and Coding to Neural Plasticity in theCentral Nervous System (F. Panetsos, A. Sanchez-Jimenez, C. Herrera-Rincon).-EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Studyin Alcoholism (V. Sakkalis, K. Marias).- Maximal Connectivity and Constraintsin the Human Brain (R.V. Belavkin).​

From the reviews: "This collection of chapters in the field of bioinformatics focuses on quantitative analysis. A number of examples accompany the methodology, so readers can easily understand how to apply the methods to solve problems. ... this book provides a comprehensive introduction to the quantitative analysis of biomedical data. It is a great handbook for beginners entering the field of bioinformatics." (Hsun-Hsien Chang, ACM Computing Reviews, January, 2013)