Giorgio Russolillo

Identification of microRNA-regulated gene networks by expression analysis of target genes.

MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.

New Perspectives in Partial Least Squares and Related Methods

New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed research from presentations during the 2012 partial least squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS is an abbreviation for Partial Least Squares and is also sometimes expanded as projection to latent structures. This is an approach for modeling relations between data matrices of different types of variables measured on the same set of objects. The twenty-two papers in this volume, which include three invited contributions from our keynote speakers, provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. Prominent scientists from around the world took part in PLS 2012 and their contributions covered the multiple dimensions of the partial least squares-based methods. These exciting theoretical developments ranged from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. In following the tradition of the six previous PLS meetings, these contributions also included a large variety of PLS approaches such as PLS metamodels, variable selection, sparse PLS regression, distance based PLS, significance vs. reliability, and non-linear PLS. Finally, these contributions applied PLS methods to data originating from the traditional econometric/economic data to genomics data, brain images, information systems, epidemiology, and chemical spectroscopy. Such a broad and comprehensive volume will also encourage new uses of PLS models in work by researchers and students in many fields.

Non-Metric Partial Least Squares

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be ad- justed for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the Non- Metric NIPALS, the Non-Metric PLS Regression and the Non-Metric PLS Path Modeling, respectively. These algorithms extend the applicability of PLS methods to data measured on different measurement scales, as well as to variables linked by non-linear relationships.

Evaluation de la présence des capacités marketing: Proposition d’un index multidimensionnel et hiérarchique

Notre ambition est de proposer un instrument multidimensionnel permettant de décrire le degré de présence des principales capacités marketing sur trois niveaux d’abstraction. Après avoir présenté le cadre théorique relatif aux capacités marketing, l’article souligne tout d’abord les limites des principales échelles proposées par Vorhies et al. (1999 ; 2009), Vorhies et Harker (2000), et Vorhies et Morgan (2003 ; 2005). Ensuite, les étapes nécessaires au développement et à la validation d’un index multidimensionnel formatif de troisième ordre sont détaillées. Sur la base d’une collecte de données réalisée auprès d’un échantillon de 199 PME françaises, la phase d’analyse de la validité convergente et discriminante de l’instrument est réalisée à l’aide de l’approche PLS aux modèles à variables latentes (PLS-PM). Enfin, la validité nomologique de l’instrument proposé est confirmée via l’étude de l’influence des capacités marketing sur la performance organisationnelle.

Evaluating the presence of marketing capabilities: A multidimensional, hierarchical index

We propose a multidimensional instrument to assess the degree of presence of marketing capabilities a firm possesses, at three levels of abstraction. We first present the theoretical framework for marketing capabilities and discuss the main scales proposed by Vorhies et al. Then, we detail the steps required to develop and validate our third-order formative instrument. We assess the convergent and discriminant validity of the proposed instrument via partial least squares path modelling (PLS-PM) applied to a sample of 199 French small- and medium-sized enterprises (SMEs). Finally, we check the nomological validity of our instrument by testing the positive effect of marketing capabilities on organisational performance.

Integrating Non-metric Data in Partial Least Squares Path Models: Methods and Application

In this chapter we discuss how to include non-metric variables (i.e., ordinal and/or nominal) in a PLS Path Model. We present the Non-Metric PLS approach for handling these type of variables, and we integrate the logistic regression into the PLS Path model for predicting binary outcomes. We discuss features and properties of these PLS Path Modeling enhancements via an application on real data. We use data collected by merging the archives of Sapienza University of Rome and the Italian Ministry of Labor and Social Policy. The analysis of this data measured quantitatively, for the first time in Italy, the impact of graduates’ Educational Performance on the first 3 years of their job career.