Aggregation-Based Feature Invention and Relational

Due to interest in social and economic networks, relational modeling is attracting increasing attention. The field of relational data mining/learning, which traditionally was dominated by logic-based approaches, has recently been extended by adapting learning methods such as naive Bayes, Baysian networks and decision trees to relational tasks. One aspect inherent to all methods of model induction from relational data is the construction of features through the aggregation of sets. The theoretical part of this work (1) presents an ontology of relational concepts of increasing complexity, (2) derives classes of aggregation operators that are needed to learn these concepts, and (3) classifies relational domains based on relational schema characteristics such as cardinality. We then present a new class of aggregation functions, ones that are particularly well suited for relational classification and class probability estimation. The empirical part of this paper demonstrates on real domain the effects on the system performance of different aggregation methods on different relational concepts. The results suggest that more complex aggregation methods can significantly increase generalization performance and that, in particular, task-specific aggregation can simplify relational prediction tasks into well-understood propositional learning problems.Information Systems Working Papers Serie

Ranking relations using analogies in biological and information networks

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects $\mathbf{S}=\{A^{(1)}:B^{(1)},A^{(2)}:B^{(2)},\ldots,A^{(N)}:B ^{(N)}\}$, measures how well other pairs A:B fit in with the set $\mathbf{S}$. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS321 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Predicting zinc binding at the proteome level

BACKGROUND: Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding properties remain difficult to predict as well as to investigate experimentally at the whole-proteome level. Consequently, the current knowledge about metalloproteins is only partial. RESULTS: The present work reports on the development of a machine learning method for the prediction of the zinc-binding state of pairs of nearby amino-acids, using predictors based on support vector machines. The predictor was trained using chains containing zinc-binding sites and non-metalloproteins in order to provide positive and negative examples. Results based on strong non-redundancy tests prove that (1) zinc-binding residues can be predicted and (2) modelling the correlation between the binding state of nearby residues significantly improves performance. The trained predictor was then applied to the human proteome. The present results were in good agreement with the outcomes of previous, highly manually curated, efforts for the identification of human zinc-binding proteins. Some unprecedented zinc-binding sites could be identified, and were further validated through structural modelling. The software implementing the predictor is freely available at: CONCLUSION: The proposed approach constitutes a highly automated tool for the identification of metalloproteins, which provides results of comparable quality with respect to highly manually refined predictions. The ability to model correlations between pairwise residues allows it to obtain a significant improvement over standard 1D based approaches. In addition, the method permits the identification of unprecedented metal sites, providing important hints for the work of experimentalists

Cloud-Based Design and Manufacturing Systems: A Social Network Analysis

Cloud-Based Design and Manufacturing (CBDM) System refers to an information and communication technology (ICT) system that facilitates design and manufacturing knowledge sharing between actors (e.g., CBDM service providers and consumers) in the distributed and collaborative socio-technical network. The aim of this study is to address the challenge of information sharing and technical communication during the CBDM product development process. Specifically, we model a CBDM system as a socio-technical network. The research questions are: (1) What measures can be used to analyze the socio-technical network generated by CBDM? (2) How to detect communities/clusters and key actors in the socio-technical network? To answer these questions, a social network analysis (SNA) approach is formulated to analyze the socio-technical network generated by CBDM systems. The results indicate that SNA allows for visualizing collaborative relationship patterns of actors as well as detecting the community structure of CBDM systems

Modeling Complex Networks For (Electronic) Commerce

NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Aggregation-Based Feature Invention and Relational

Due to interest in social and economic networks, relational modeling is attracting increasing attention. The field of relational data mining/learning, which traditionally was dominated by logic-based approaches, has recently been extended by adapting learning methods such as naive Bayes, Baysian networks and decision trees to relational tasks. One aspect inherent to all methods of model induction from relational data is the construction of features through the aggregation of sets. The theoretical part of this work (1) presents an ontology of relational concepts of increasing complexity, (2) derives classes of aggregation operators that are needed to learn these concepts, and (3) classifies relational domains based on relational schema characteristics such as cardinality. We then present a new class of aggregation functions, ones that are particularly well suited for relational classification and class probability estimation. The empirical part of this paper demonstrates on real domain the effects on the system performance of different aggregation methods on different relational concepts. The results suggest that more complex aggregation methods can significantly increase generalization performance and that, in particular, task-specific aggregation can simplify relational prediction tasks into well-understood propositional learning problems.Information Systems Working Papers Serie