Editorial Advances in Computational Methods for Genetic Diseases Francesco Camastra, 1 Roberto Amato, 2 Maria Donata Di Taranto, 3 and Antonino Staiano 1 1 Department of Science and Technology, University of Naples Parthenope, Centro Direzionale, Isola C4, 80143 Napoli, Italy 2 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK 3 IRCCS SDN, Via E. Gianturco 113, 80143 Napoli, Italy Correspondence should be addressed to Francesco Camastra; francesco.camastra@uniparthenope.it Received 10 May 2015; Accepted 10 May 2015 Copyright © 2015 Francesco Camastra et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Genetic diseases are a wide group of diseases in which the etiopathogenesis is caused by or related to genetic factors. e role of genetics in the disease development can be more or less relevant depending on the specific characteristics of the disease, and a wide spectrum of complexity exists. Monogenic diseases, for example, are directly caused by defects in a specific gene whereas complex and polygenic dis- eases are generally caused by the interactions between multi- ple genes or between genetic and environmental factors. To the last category belong many forms of cancer, an uncon- trolled growth of cells with alterations of the genetic mate- rials. In the last decade, a large amount of experimental data has become available, so the identification of strategies to process and, most importantly, interpret them is crucial. e massive volume of data, both in terms of quantity and of dimension- ality, and their heterogeneity and low signal-to-noise ratio are just some of the most obvious challenges that they present. To give an example, single nucleotide DNA mutations are one of the most common factors analysed in relation to the devel- opment of a genetic disease. However, this sometimes trans- lates into dealing with millions of variants measured across thousands of individuals, where only a handful are infor- mative. In fact, other more complex factors, such as gene expression, could play a significant role. e aim of this special issue is to review the recent advances in computational methods concerned with genetic diseases. e issue received sixteen submissions; each one was referred by at least two international reviewers that we warmly thank for their time. Six papers have been accepted for the publication. “A New Approach for Mining Order-Preserving Subma- trices Based on all Common Subsequences” by Y. Xue et al. proposes, in the context of gene expression data, a pattern- based subspace clustering or OPSM (order-preserving sub- matrix model), based on frequent sequential pattern. e approach has been experimentally proven to be able to discover the biological significant OPSMs and deep OPSMs exhaustively. “Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephro- genic Diabetes Insipidus” by S. Grunert and D. Labudde is devoted to the application of a high-throughput analysis method based on motif conservation among proteins of the same protein family for analysis of interacting sequences. is investigation can help to analyze the pathogenic impact of mutations causing alterations in interacting regions of a protein. is analysis has been applied on membrane proteins, in particular to the aquaporin 2 whose mutants are involved in nephrogenic diabetes insipidus. “Unified Modeling of Familial Mediterranean Fever and Cryopyrin Associated Periodic Syndromes” by Y. Bozkurt et al. describes a unifying dynamical model for Familial Mediterranean Fever (FMF) and Cryopyrin Associated Peri- odic Syndromes (CAPS) in the form of coupled nonlinear ordinary differential equations. e authors perform a com- prehensive bifurcation analysis of the model and show that it exhibits three modes, capturing the healthy, FMF, and CAPS cases. ey present extensive simulation results for the model that match clinical observations. “Enhancing the Lasso Approach for Developing a Sur- vival Prediction Model Based on Gene Expression Data” by S. Kaneko et al. presents a novel improvement to the lasso Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Article ID 645649