Results 1 - 10 of 61

CONCENTRATION AND REGULARIZATION OF RANDOM GRAPHS

by Can M. Le, Roman Vershynin
"... Abstract. This paper studies how close random graphs are typically to their expectations. We interpret this question through the concentration of the adjacency and Laplacian matri-ces in the spectral norm. We study inhomogeneous Erdös-Rényi random graphs on n vertices, where edges form independent ..."
Abstract - Add to MetaCart
below by d by adding weight d/n to all edges, then the resulting Laplacian concentrates with the op-timal rate: ‖L(A′)−L(EA′) ‖ = O(1/√d). Our approach is based on Grothendieck-Pietsch factorization, using which we construct a new decomposition of random graphs. These re-sults improve and considerably

H (2007): Network-constrained regularization and variable selection for analysis of genomic data. UPenn Biostatistics Working Paper

by Caiyan Li, Hongzhe Li
"... Motivation: Graphs or networks are common ways of depicting information. In biology in particular, many different biological processes are represented by graphs, such as regulatory networks or metabolic pathways. This kind of a priori information gathered over many years of biomedical research is a ..."
Abstract - Cited by 91 (5 self) - Add to MetaCart
useful supplement to the standard numerical genomic data such as microarray gene expression data. How to incorporate information encoded by the known biological networks or graphs into analysis of numerical data raises interesting statistical challenges. In this paper, we introduce a network

Aligned graph classification with regularized logistic regression

by Brian Quanz, Jun Huan - In Proc. 2009 SIAM International Conference on Data Mining
"... Data with intrinsic feature relationships are becoming abundant in many applications including bioinformatics and sensor network analysis. In this paper we consider a classification problem where there is a fixed and known binary relation defined on the features of a set of multivariate random varia ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
. To incorporate the feature relationship, we extend logistic regression and use a regularization term that includes the normalized Laplacian of the graph, similar to the L2 regularization, deriving a modified optimization problem and solution. We demonstrate the effectiveness of our method and compare it to other

Systematic topology analysis and generation using degree correlations

by Priya Mahadevan, Dmitri Krioukov, Kevin Fall, Amin Vahdat - In SIGCOMM
"... Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph’s resilience to failure or its routing efficiency. Knowledge of appropriate metric values may inf ..."
Abstract - Cited by 94 (7 self) - Add to MetaCart
Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph’s resilience to failure or its routing efficiency. Knowledge of appropriate metric values may

RESEARCH Open Access Molecular pathway identification using biological network-regularized logistic models

by Wen Zhang, Ying-wooi Wan, Genevera I Allen, Kaifang Pang, Matthew L Anderson, Zhandong Liu
"... Background: Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, ha ..."
Abstract - Add to MetaCart
, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature. Results: We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease

Protein Function Prediction via Laplacian Network Partitioning Incorporating Function Category Correlations

by Hua Wang, Heng Huang, Chris Ding - PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Understanding the molecular mechanisms of life requires decoding the functions of the proteins in an organism. Various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. A fundamental challenge of the post-genomic era is to assign biol ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
biological functions to all the proteins encoded by the genome using high-throughput biological data. To address this challenge, we propose a novel Laplacian Network Partitioning incorporating function category Correlations (LNPC) method to predict protein function on proteinprotein interaction (PPI

Algorithms for gene regulatory networks reconstruction

by Nikolai Maksimov
"... MASTER Algorithms for gene regulatory networks reconstruction Maksimov, N.V. Award date: 2015 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available ..."
Abstract - Add to MetaCart
one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Abstract Gene regulatory network (GRN) inference is a central problem in systems biology, that has

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btn081 Systems biology Network-constrained regularization and variable selection for analysis of genomic data

by Caiyan Li, Hongzhe Li
"... Motivation: Graphs or networks are common ways of depicting information. In biology in particular, many different biological processes are represented by graphs, such as regulatory networks or metabolic pathways. This kind of a priori information gathered over many years of biomedical research is a ..."
Abstract - Add to MetaCart
useful supplement to the standard numerical genomic data such as microarray gene-expression data. How to incorporate information encoded by the known biological networks or graphs into analysis of numerical data raises interesting statistical challenges. In this article, we introduce a network

Link Prediction in Biological Networks using Multi-Mode Exponential Random Graph Models

by Ali Shojaie
"... We propose a novel multi-mode exponential random graph model for supervised prediction of gene networks, coupled with a penalized estimation framework for improved prediction performance. The proposed framework facilitates the analysis of gene networks with multiple edge types, and provides a system ..."
Abstract - Add to MetaCart
We propose a novel multi-mode exponential random graph model for supervised prediction of gene networks, coupled with a penalized estimation framework for improved prediction performance. The proposed framework facilitates the analysis of gene networks with multiple edge types, and provides a

Clustering with Multi-Layer Graphs: A Spectral Perspective

by Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai Nefedov , 2011
"... Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of ..."
Abstract - Cited by 8 (1 self) - Add to MetaCart
of the multi-layer graph for improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on joint matrix factorization and graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely
Results 1 - 10 of 61