2664026
Simulating the Growth of Viruses
To explore how the genome of an organism defines its growth, we have developed a computer simulation for the intracellular growth of phage T7 on its E. coli host. Our simulation, which incorporates 30 years of genetic, biochemical, physiological, and biophysical data, is used here to study how the intracellular resources of the host, determined by the specific growth rate of the host, contribute toward phage development. It is also used to probe how changes in the linear organization of genetic elements on the T7 genome can affect T7 development. Further, we show how time-series trajectories of T7 mRNA and protein levels generated by the simulation may be used as raw data to test data-mining strategies, specifically, to identify partners in protein-protein interactions. Finally, we suggest how generalization of this work can lead to a knowledge-driven simulation for the growth of any virus.
1277960
Modeling biological systems using Dynetica-a simulator of dynamic networks
UNLABELLED
We present Dynetica, a user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations.


AVAILABILITY AND SUPPLEMENTARY INFORMATION
Dynetica 1.0, example models, and the user's guide are available at http://www.its.caltech.edu/~you/Dynetica/Dynetica_page.htm
431999
Q2C@UST: our winning solution to query classification in KDDCUP 2005
In this paper, we describe our ensemble-search based approach, Q2C@UST (http://webprojectl.cs.ust.hk/q2c/), for the query classification task for the KDDCUP 2005. There are two aspects to the key difficulties of this problem: one is that the meaning of the queries and the semantics of the predefined categories are hard to determine. The other is that there are no training data for this classification problem. We apply a two-phase framework to tackle the above difficulties. Phase I corresponds to the training phase of machine learning research and phase II corresponds to testing phase. In phase I, two kinds of classifiers are developed as the base classifiers. One is synonym-based and the other is statistics based. Phase II consists of two stages. In the first stage, the queries are enriched such that for each query, its related Web pages together with their category information are collected through the use of search engines. In the second stage, the enriched queries are classified through the base classifiers trained in phase I. Based on the classification results obtained by the base classifiers, two ensemble classifiers based on two different strategies are proposed. The experimental results on the validation dataset help confirm our conjectures on the performance of the Q2C@UST system. In addition, the evaluation results given by the KDDCUP 2005 organizer confirm the effectiveness of our proposed approaches. The best F1 value of our two solutions is 9.6% higher than the best of all other participants' solutions. The average F1 value of our two submitted solutions is 94.4% higher than the average F1 value from all other submitted solutions.
43330192
Impact of bursty error rates on the performance of wireless local area network (WLAN)
Abstract With an increasing popularity of DCF based wireless LAN, the modeling of 802.11 distributed coordination function (DCF) has attracted lots of research attention. Existing analysis of 802.11 DCF has been focused on the determination of the throughput and the packet delay under saturated traffic and ideal channel conditions. Although some recent papers address the saturated performance under a simple uniform error model, they can hardly capture the impact of bursty characteristics of wireless fading on the performance of 802.11 DCF. This paper presents exact formulae for the throughput and the delay in DCF for various traffic conditions when either saturated or unsaturated traffic load is present. A two-state Markov channel model is incorporated to present the bursty characteristics of channel errors. With our analysis, the impact of bursty channel error on unsuccessful transmission probability and the DCF performance can be determined. The results of our analytical framework reveal that the four-way handshaking scheme does not improve throughput substantially for light traffic load. However, for heavy traffic load, the four-way handshaking scheme is advantageous as compared to the basic access scheme. Also, extensive simulation is done to substantiate the accuracy of our analytical model.
456221
Integrating hidden Markov models and spectral analysis for sensory time series clustering
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to data mining, including uneven sequence lengths, multi-dimensionality and high levels of noise. We adopt a principled approach, by first transforming all the data into an equal-length vector form while keeping as much temporal information as we can, and then applying dimensionality and noise reduction techniques such as spectral clustering to the transformed data. Experimental evaluation on synthetic and real data shows that our proposed approach outperforms standard model-based clustering algorithms for time series data.
1805342
Adaptive Temporal Radio Maps for Indoor Location Estimation
In this paper, we present a novel method to adapt the temporal radio maps for indoor location estimation by offsetting the variational environmental factors using data mining techniques and reference points. Environmental variations, which cause the signals to change from time to time even at the same location, present a challenging task for indoor location estimation in the IEEE 802.11b infrastructure. In such a dynamic environment, the radio maps obtained in one time period may not be applicable in other time periods. To solve this problem, we apply a regression analysis to learn the temporal predictive relationship between the signal-strength values received by sparsely located reference points and that received by the mobile device. This temporal prediction model can then be used for online localization based on the newly observed signal-strength values at the client side and the reference points. We show that this technique can effectively accommodate the variations of signal-strength values over different time periods without the need to rebuild the radio maps repeatedly. We also show that the location of mobile device can be accurately determined using this technique with lower density in the distribution of the reference points