Pattern recognition on antigenantibody interactions from protein microarrays based on data mining and bioinformatics analysis Understanding the mechanisms of interaction between antigenantibody systems is one of the most relevant problems in the field of Immunology, since, supported by computational tools and data mining techniques, it would be possible to implement new antibody design tools. As a first approach and taking advantage of microarray techniques, a biological pattern recognition pipeline is presented from multiple ProtoArray chips, based on data mining techniques and bioinformatic analysis. This new approach, proposes 3 key stages in data processing: cleaning, statisticalfrequency clustering of antigens and characterization by bioinformatic methods. This methodology was tested in a set of 370,000 interactions (45 antibodies, 8,000 antigens), achieving an effective filtering and grouping, in addition, a group obtained is characterized, in order to test the proposed bioinformatic methods. It is expected that this new method allows the identification of patterns in a simple and efficient way from multidimensional sets generated by protein microarrays.

Improving Allergenic Protein Prediction Using Physicochemical Features on NonRedundant Sequences Despite extensive studies in allergen prediction, current approaches still have room for performance improvement and suffer from the problem of lack of interpretable biological features. Thus, developments of allergen prediction method from sequences have become highly important to facilitate in silico vaccine design. In this study, we propose a systematic approach to predict allergenic proteins by incorporating sequence and physicochemical properties in machine learning algorithms. In addition, predictive performance of previous studies could be overestimated due to high redundancy in the data sets. Therefore, we reduce sequence redundancy in the data set and experiment results show that we achieve better predictive performance when compared with other approaches. This study can help discover new prophylactic and therapeutic vaccines for diseases. Moreover, we analyze immunological features that can provide valuable insights into immunotherapies of allergy and autoimmune diseases in translational bioinformatics.