Advances in high-throughput technologies allow for measurements of many types of omics data, yet the meaningful integration of several different data types remains a significant challenge. Another important and difficult problem is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. Here we present a novel approach, calledperturbation clustering for dataintegration and diseasesubtyping (PINS), which is able to address both challenges. The framework has been validated on thousands of cancer samples, using gene expression, DNA methylation, noncoding microRNA, and copy number variation data available from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. This simultaneous subtyping approach accurately identifies known cancer subtypes and novel subgroups of patients with significantly different survival profiles. The results were obtained from genome-scale molecular data without any other type of prior knowledge. The approach is sufficiently general to replace existing unsupervised clustering approaches outside the scope of bio-medical research, with the additional ability to integrate multiple types of data.

Information Retrieval (IR) techniques have gained wide-spread acceptance as a method for automating traceability recovery. These techniques recover links between software artifacts based on their textual similarity, i.e., the higher the similarity, the higher the likelihood that there is a link between the two artifacts. A common problem with all IR-based techniques is filtering out noise from the list of candidate links, in order to improve the recovery accuracy. Indeed, software artifacts may be related in many ways and the textual information captures only one aspect of their relationships. In this paper we propose to leverage code ownership information to capture relationships between source code artifacts for improving the recovery of traceability links between documentation and source code. Specifically, we extract the author of each source code component and for each author we identify the “context” she worked on. Thus, for a given query from the external documentation we compute the similarity between it and the context of the authors. When retrieving classes that relate to a specific query using a standard IR-based approach we reward all the classes developed by the authors having their context most similar to the query, by boosting their similarity to the query. The proposed approach, named TYRION (TraceabilitY link Recovery using Information retrieval and code OwNership), has been instantiated for the recovery of traceability links between use cases and Java classes of two software systems. The results indicate that code ownership information can be used to improve the accuracy of an IR-based traceability link recovery technique.

MicroRNAs (miRNAs) are small non-coding RNA molecules whose primary function is to regulate the expression of gene products via hybridization to mRNA transcripts, resulting in suppression of translation or mRNA degradation. Although miRNAs have been implicated in complex diseases, including cancer, their impact on distinct biological pathways and phenotypes is largely unknown. Current integration approaches require sample-matched miRNA/mRNA datasets, resulting in limited applicability in practice. Since these approaches cannot integrate heterogeneous information available across independent experiments, they neither account for bias inherent in individual studies, nor do they benefit from increased sample size. Here we present a novel framework able to integrate miRNA and mRNA data (vertical data integration) available in independent studies (horizontal meta-analysis) allowing for a comprehensive analysis of the given phenotypes. To demonstrate the utility of our method, we conducted a meta-analysis of pancreatic and colorectal cancer, using 1,471 samples from 15 mRNA and 14 miRNA expression datasets. Our two-dimensional data integration approach greatly increases the power of statistical analysis and correctly identifies pathways known to be implicated in the phenotypes. The proposed framework is sufficiently general to integrate other types of data obtained from high-throughput assays.

Water distribution system minimum-cost design is probably the most commonly addressed modeling problem in hydraulic engineering. In the last decades, metaheuristic algorithms have been established as the most effective methods to solve this problem. However, because of their stochastic nature, a huge number of hydraulic simulations are needed before adequate results are found. An alternative approach based on the understanding of the hydraulic behavior of optimal designs is presented in this paper. It attempts to replace the tiresome search process undertaken by metaheuristics. The methodology focuses on establishing efficient ways in which energy is dissipated and flow is distributed. The algorithm was tested on two benchmark systems reaching similar results to those of previous works but in an amply reduced number of iterations. This novel approach shows plenty of potential in other water distribution modeling applications.

MicroRNAs play important roles in the development of many complex diseases. Because of their importance, the analysis of signaling pathways including miRNA interactions holds the potential for unveiling the mechanisms underlying such diseases. However, current signaling pathway databases are limited to interactions between genes and ignore miRNAs. Here, we use the information on miRNA targets to build a database of miRNA-augmented pathways (mirAP), and we show its application in the contexts of integrative pathway analysis and disease subtyping. Our miRNA-mRNA integrative pathway analysis pipeline incorporates a topology-aware approach that we previously implemented. Our integrative disease subtyping pipeline takes into account survival data, gene and miRNA expression, and knowledge of the interactions among genes. We demonstrate the advantages of our approach by analyzing nine sample-matched datasets that provide both miRNA and mRNA expression. We show that integrating miRNAs into pathway analysis results in greater statistical power, and provides a more comprehensive view of the underlying phenomena. We also compare our disease subtyping method with the state-of-the-art integrative analysis by analyzing a colorectal cancer database from TCGA. The colorectal cancer subtypes identified by our approach are significantly different in terms of their survival expectation. These miRNA-augmented pathways offer a more comprehensive view and a deeper understanding of biological pathways. A better understanding of the molecular processes associated with patients' survival can help to a better prognosis and an appropriate treatment for each subtype.

With the explosion of high-throughput data, an effective integrative analysis is needed to decipher the knowledge accumulated in multiple studies. However, batch effects, patient heterogeneity, and disease complexity all complicate the integration of data from different sources. Here we introduce TOMAS, a novel meta-analysis framework that transforms the challenging meta-analysis problem into a set of standard analysis problems that can be solved efficiently. This framework utilizes techniques based on both p-values and effect sizes to identify differentially expressed genes and their expression change on a genome-scale. The computed statistics allow for topology-aware pathway analysis of the given phenotypes, where topological information of genes is taken into consideration. We compare TOMAS with four meta-analysis approaches, as well as with three dedicated pathway analysis approaches that employ multiple datasets (MetaPath). The eight approaches have been tested on 609 samples from 9 Alzheimer's studies conducted in independent labs for different sets of patients and tissues. We demonstrate that the topology based meta-analysis framework overcomes noise and bias to identify pathways that are known to be implicated in Alzheimer's disease. While presented here in a genomic data analysis application, the proposed framework is sufficiently general to be applied in other research areas.