Inferring a Transcriptional Regulatory Network from Gene Expression Data Using Nonlinear Manifold Embedding

Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators of transcription as well as proteins and enzymes shaping the DNA template. Such intrinsic multi-dimensionality makes uncovering connectivity patterns difficult and unreliable and it calls for adoption of methodologies commensurate with the underlying organization of the data source. Here we present a new computational method that predicts interactions between transcription factors and target genes using a compendium of microarray gene expression data and the knowledge of known interactions between genes and transcription factors. The proposed method called Kernel Embedding of REgulatory Networks (KEREN) is based on the concept of gene-regulon association and it captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct gene regulatory interactions in the model bacteria E.coli on a genome-wide scale. Our method not only yields accurate prediction of verifiable interactions, which outperforms on certain metrics comparable methodologies, but also demonstrates the utility of a geometric approach to the analysis of high-dimensional biological data. We also describe the general application of kernel embedding techniques to some other function and network discovery algorithms

Behavioral analysis of anisotropic diffusion in image processing

©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.541424In this paper, we analyze the behavior of the anisotropic diffusion model of Perona and Malik (1990). The main idea is to express the anisotropic diffusion equation as coming from a certain optimization problem, so its behavior can be analyzed based on the shape of the corresponding energy surface. We show that anisotropic diffusion is the steepest descent method for solving an energy minimization problem. It is demonstrated that an anisotropic diffusion is well posed when there exists a unique global minimum for the energy functional and that the ill posedness of a certain anisotropic diffusion is caused by the fact that its energy functional has an infinite number of global minima that are dense in the image space. We give a sufficient condition for an anisotropic diffusion to be well posed and a sufficient and necessary condition for it to be ill posed due to the dense global minima. The mechanism of smoothing and edge enhancement of anisotropic diffusion is illustrated through a particular orthogonal decomposition of the diffusion operator into two parts: one that diffuses tangentially to the edges and therefore acts as an anisotropic smoothing operator, and the other that flows normally to the edges and thus acts as an enhancement operator

Broadband angle of arrival estimation methods in a polynomial matrix decomposition framework

A large family of broadband angle of arrival estimation algorithms are based on the coherent signal subspace (CSS) method, whereby focussing matrices appropriately align covariance matrices across narrowband frequency bins. In this paper, we analyse an auto-focussing approach in the framework of polynomial covariance matrix decompositions, leading to comparisons to two recently proposed polynomial multiple signal classification (MUSIC) algorithms. The analysis is complemented with numerical simulations

Affordances and Information Systems Research: Taking Stock and Moving Forward

The term affordance appears with increasing frequency in the Information Systems (IS) literature. Nevertheless, those who study information technologies/information systems (IT/IS) via the affordance lens often have different views about its origin, meaning, and appropriate application in IS research. In turn, not spelling out the related assumptions and boundaries inherent in these diverse views may have hindered a wider and more cumulative adoption of the affordance lens in IS research. This paper offers a potential solution by (1) synthesizing the ecological psychology literature to suggest five key modules of the affordance concept relevant to IS research and (2) taking stock of IS research that has employed the affordance concept and classifying it according to its focus on three key affordance elements: IT artifact, user, and context. Finally, this paper presents a set of challenges, opportunities, and recommendations regarding how IS researchers can advance affordance-based research in the field

Conflating Relevance with Practical Significance and Other Issues: Commentary on Sen, Smith, and Van Note’s “Statistical Significance Versus Practical Importance in Information Systems Research”

Expanding on the current debate on the issues of statistical and practical significance in information systems research, where the article by Sen, Smith, and Van Note is a recent contribution, this commentary cautions against conflating relevance with practical significance. We emphasize that relevance is 1) about the real-world usefulness of research findings rather than their impressiveness for the researcher audience, 2) an essential quality of research spanning beyond its findings and not merely limited to statistical studies, and 3) determined by nonacademics rather than academics. We also comment on other aspects of the article by Sen et al., such as the term “practical importance,” the treatment of effect size measures, and the presentation of “marginal effects.