18781820
QSAR in grossly underdetermined systems: Opportunities and issues
Regression in grossly underdetermined systems has emerged as an important means for understanding molecular activity via comparative molecular field analysis (CoMFA) and other quantitative structure activity relationship (QSAR) studies. But this methodology has applications in much broader areas; for example, near-infrared spectroscopy, mutational enzyme activity studies including protein folding rates to determine which sites are important for determining conformation, and analyses of gene expression data from chip arrays. An error analysis which answers questions concerning the quality of the predictivity, the relative importance of each descriptor, the quality of the estimates of the contribution by each descriptor, and the number of independent components expressed by the associated data is indispensable in understanding whether some particular set of structure variables is important in defining the mechanisms driving the chemical or biological activities. This paper reviews opportunities for QSAR studies. It also considers the analytical aspects of error analysis in least-squares regression, and contrasts principal component regression (PCR) and partial least-squares (PLS) procedures with cross-validation on the issues of error analysis (e.g., the quality of the contribution estimates for each structure descriptor). Further, a methodology for selecting optimal subsets of components in PCR is presented.
18106801
Mining Protein Sequences for Motifs
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.
11110395
In Vitro Implementation of Finite-State Machines
We explore the information processing capabilities and efficiency of DNA computations by giving two different types of implementations of finite-state machines. A ligation-based approach allows input of arbitrary length and can be readily implemented with current biotechnology, but requires sequential input feed and different molecules for different machines. In a second implementation not based on ligation, transitions are represented by reusable molecules, and the input, coded as a molecule, can be introduced at once. We extend the technique for programmable fault-tolerant implementation of nondeterministic finite-state machines by enforcings the basic conditions in the subset constructions that permit efficient computation. All implementations allow optical extraction of the status of the machine.
13799231
Improving molecular cancer class discovery through sparse non-negative matrix factorization
MOTIVATION
Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.


RESULTS
We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
17160551
Pattern discovery on character sets and real-valued data: linear bound on irredundant motifs and an efficient polynomial time algorithm
Given an input sequence of data, a motif is a repeating pattern, possibly interspersed with "dont care" characters. The data could be a sequence of characters or sets of characters or even real values. In the first two cases, the number of motifs could potentially be exponential in the size of the input sequence and in the third case there could be infinite number of motifs (assuming two real numbers are equal if they are within some specified 5 > 0 of each other). However, it has been shown in some motif based applications, such as multiple sequence aligz~nent [Par98, PFR99], that a small subset of these motifs capture all the relevant information. Also, from an information-theoretic viewpoint, the special motifs represent the sets with low co-relation, hence of higher informational value. In this paper, we show, for any sequence with n characters, there exists only a linear (or no more than 3n) number of these special motifs and every other motif can be constructed from this set. We term these special motifs as irredundant motifs. We also present an efficient polynomial time algorithm to generate these motifs. This bound on the number of useful motifs ~ves validation to motif-based approaches, since the total number of irredundant motifs does not explode. This result is potentially of significance to most applications that use pattern discovery as the basic engine such as data mining, clustering, matching and others.
5391334
Sequence homology detection through large scale pattern discovery
Extended Abstract We describe a new approach for idenhifling sequence similatity between a query sequence and a data base of proteins. The central idea is the use of a set ofpattems obtainedfiom the underlying data base through an one-time computation. These patterns are subsequently searchedfor on every query sequence presented to the system. A pattern matched by a region of the quev pinpoints to a potential local similarity between that reg+on and all the data base sequences also matching that pattern. By using a set of prudently chosen patterns, the tool presented in this work is able to discover weak but biologically important similarities.
15420304
Incremental gradient descent imputation method for missing data in learning classifier systems
Learning with incomplete or missing data has been a major challenge in learning classifier system. One method for covering missing data is imputing missing values based on the statistic of known values. Another is marking them matching arbitrary case. A new approach using incremental gradient descent imputation model is proposed in this paper, which use the relationship among variables to estimate the missing value. And, some experiments are conducted in order to compare the performance of new approach and other classical covering methods.
181464
An Analysis of Phase Transition in NK Landscapes
In this paper, we analyze the decision version of the NK landscape model from the perspective of threshold phenomena and phase transitions under two random distributions, the uniform probability model and the fixed ratio model. For the uniform probability model, we prove that the phase transition is easy in the sense that there is a polynomial algorithm that can solve a random instance of the problem with the probability asymptotic to 1 as the problem size tends to infinity. For the fixed ratio model, we establish several upper bounds for the solubility threshold, and prove that random instances with parameters above these upper bounds can be solved polynomially. This, together with our empirical study for random instances generated below and in the phase transition region, suggests that the phase transition of the fixed ratio model is also easy.
14861370
On transforming statistical models for non-frontal face verification
We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.
27306380
A case for exploiting self-similarity of network traffic in TCP congestion control
Analytical and empirical studies have shown the self-similar traffic can have detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to better design resource control algorithms. In this paper, we explore the issueof exploiting the self-similar characteristics of network traffic in TCP congestion control. We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to predict the future traffic. We then devise a novel scheme, called TCP with traffic prediction (TCP-TP), that exploits the prediction result to infer, in the context of AIMD steady state dynamics, the optimal operational point at which a TCP connection should operate. Through analytical reasoning, we show that the impact of prediction errors on fairness is minimal. We also conduct ns-2 simulation and freeBSD, 4.1-based implementation studies to validate the design and to demonstrate the performance improvement in terms of packet loss ratio and throughput attained by connections.
10779366
An efficient adaptive focused crawler based on ontology learning
The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.