20793791
Longitudinal data model selection
In longitudinal data with correlated errors, we apply the likelihood and residual likelihood approaches to obtain the corrected Akaike information criterion (AICc) and the residual information criterion (RIC), respectively. Simulation studies show that AICc outperforms the Akaike information criterion (AIC) when the numbers of subjects and repeated observations are small, and RIC is superior to the Bayesian information criterion (BIC) when the signal-to-noise ratio is moderate to large. We illustrate the practical use of these selection criteria with an empirical example for modeling the serum cholesterol measured at six time occasions.
5082292
Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information
MOTIVATION
It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.


RESULTS
We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.
13836655
Dimension reduction methods for microarrays with application to censored survival data
MOTIVATION
Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles.


RESULTS
We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis.


AVAILABILITY
R programs are available upon request from the authors.


SUPPLEMENTARY INFORMATION
http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.
37524397
Cluster-based estimation for sufficient dimension reduction
Sufficient dimension reduction (SDR) approaches such as ordinary least squares (OLS), sliced inverse regression, sliced average variance estimation and principal Hessian directions all require a linearity condition on the marginal distribution of the predictors X. This can be a significant limitation in practice. In this article we propose the use of clustering on the predictor space with the goal of breaking the nonlinearity among the predictors. We then estimate the dimension reduction subspace by combining estimates from individual clusters. OLS is used as a demonstration of the cluster-based estimation approach. Simulations have shown that the proposed method effectively mitigates the effects of nonlinearity; it thus has the potential to widen the applicative scope of existing SDR techniques.
25960291
DDoS Attack Detection and Wavelets
Abstract
This paper presents a systematic method for DDoS attack detection. DDoS attack can be considered a system anomaly or misuse from which abnormal behavior is imposed on network traffic. Attack detection can be performed via abnormal behavior identification. Network traffic characterization with behavior modeling could be a good indication of attack detection. Aggregated traffic has been found to be strong bursty across a wide range of time scales. Wavelet analysis is able to capture complex temporal correlation across multiple time scales with very low computational complexity. We utilize energy distribution based on wavelet analysis to detect DDoS attack traffic. Energy distribution over time will have limited variation if the traffic keeps its behavior over time (i.e. attack-free situation) while an introduction of attack traffic in the network will elicit significant energy distribution deviation in a short time period. Our experimental results with typical Internet traffic trace show that energy distribution variance markedly changes, causing a “spike” when traffic behaviors are affected by DDoS attack. In contrast, normal traffic exhibits a remarkably stationary energy distribution. In addition, this spike in energy distribution variance can be captured in the early stages of an attack, far ahead of congestion build-up, making it an effective detection of the attack.

26302874
Application of the GA/KNN method to SELDI proteomics data
SUMMARY
Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data.


AVAILABILITY
The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm
6775847
A first-principles approach to understanding the internet's router-level topology
A detailed understanding of the many facets of the Internet's topological structure is critical for evaluating the performance of networking protocols, for assessing the effectiveness of proposed techniques to protect the network from nefarious intrusions and attacks, or for developing improved designs for resource provisioning. Previous studies of topology have focused on interpreting measurements or on phenomenological descriptions and evaluation of graph-theoretic properties of topology generators. We propose a complementary approach of combining a more subtle use of statistics and graph theory with a first-principles theory of router-level topology that reflects practical constraints and tradeoffs. While there is an inevitable tradeoff between model complexity and fidelity, a challenge is to distill from the seemingly endless list of potentially relevant technological and economic issues the features that are most essential to a solid understanding of the intrinsic fundamentals of network topology. We claim that very simple models that incorporate hard technological constraints on router and link bandwidth and connectivity, together with abstract models of user demand and network performance, can successfully address this challenge and further resolve much of the confusion and controversy that has surrounded topology generation and evaluation.
9117483
Understanding Internet topology: principles, models, and validation
Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet.
623541
A multiresolution volume rendering framework for large-scale time-varying data visualization
We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a large-scale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a time-space partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve load-balanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.