8735726
Explore Biological Pathways from Noisy Array Data by Directed Acyclic Boolean Networks
We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.
11083681
MARD: a new method to detect differential gene expression in treatment-control time courses
MOTIVATION
Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses.


RESULTS
Instead of attempting to 'align' and compare the two time courses directly, we first convert the treatment and control time courses into neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks. To verify our method, we apply it to two treatment-control time course datasets. The results are consistent with the previous results and also give some new biologically meaningful findings.


AVAILABILITY
The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/
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
14092393
Lazy Approximation for Solving Continuous Finite-Horizon MDPs
Solving Markov decision processes (MDPs) with continuous state spaces is a challenge due to, among other problems. the well-known curse of dimensionality. Nevertheless, numerous real-world applications such as transportation planning and telescope observation scheduling exhibit a critical dependence on continuous states. Current approaches to continuous-state MDPs include discretizing their transition models. In this paper, we propose and study an alternative, discretization-free approach we call lazy approximation. Empirical study shows that lazy approximation performs much better than discretization, and we successfully applied this new technique to a more realistic planetary rover planning problem.
14498579
Accurate anchoring alignment of divergent sequences
MOTIVATION
Obtaining high quality alignments of divergent homologous sequences for cross-species sequence comparison remains a challenge.


RESULTS
We propose a novel pairwise sequence alignment algorithm, ACANA (ACcurate ANchoring Alignment), for aligning biological sequences at both local and global levels. Like many fast heuristic methods, ACANA uses an anchoring strategy. However, unlike others, ACANA uses a Smith-Waterman-like dynamic programming algorithm to recursively identify near-optimal regions as anchors for a global alignment. Performance evaluations using a simulated benchmark dataset and real promoter sequences suggest that ACANA is accurate and consistent, especially for divergent sequences. Specifically, we use a simulated benchmark dataset to show that ACANA has the highest sensitivity to align constrained functional sites compared to BLASTZ, CHAOS and DIALIGN for local alignment and compared to AVID, ClustalW, DIALIGN and LAGAN for global alignment. Applied to 6007 pairs of human-mouse orthologous promoter sequences, ACANA identified the largest number of conserved regions (defined as over 70% identity over 100 bp) compared to AVID, ClustalW, DIALIGN and LAGAN. In addition, the average length of conserved region identified by ACANA was the longest. Thus, we suggest that ACANA is a useful tool for identifying functional elements in cross-species sequence analysis, such as predicting transcription factor binding sites in non-coding DNA.


AVAILABILITY
ACANA software and test sequence data are publicly available at http://BioMedEmpire.org/
83373
Reasoning with large numbers of individuals moves on: extending the instance store
The Semantic Web [2] aims at enabling Web resources to be accessible to automated processes by adding “semantic annotations”—metadata (data about data) that describes their content. It is envisaged that the semantics in semantic annotations will be given by ontologies [8], which will provide a source of precisely defined terms (vocabularies) that are amenable to automated reasoning. A standard for expressing ontologies in the Semantic Web has already emerged: the ontology language OWL [4], which recently became a W3C proposed recommendation. One of the main features of OWL is that there is a direct correspondence between (two of the three “species” of) OWL and Description Logics (DLs) [12]. This means that reasoning procedures from DLs can be used in order to infer useful facts from ontology based annotations in OWL. In particular, since annotations are usually defined as individual instances of ontology classes, it is reasonable then to use ABox reasoning (the DL term for reasoning about individuals) to reason about annotations [3]. However, developing a practical ABox reasoner for such annotations is difficult. This difficulty arises not only from the computational complexity of ABox reasoning, but also from the fact that the number of annotations might be extremely large. On the one hand, the task of reasoning with large amounts of individuals is difficult even for the state-of-art optimised ABox reasoner RACER [9]. On the other hand, efficient management of very large volumes of data is well known to be a stronghold of relational databases. The approach of combining relational databases with Description Logics to deal with large amounts of data was, therefore, proposed in [11] which we call an Instance Store. The Instance Store provides an infrastructure for reasoning with large numbers of individuals. It combines a DL reasoner with a database in order to provide large scale storage of instance data, and sound and complete answers to instance retrieval queries for arbitrary query concepts. However, in order to simplify the integrated architecture, and to provide acceptable query answering performance, the current Instance Store only supports ABox axioms asserting that a given individual is an instance of a given (possibly complex) concept (e.g., John is an instance of the class of persons having at least one female child); but it forbids axiom assertions involving role relationships between pairs of individuals (e.g., an assertion that Mary is a child of John). Certainly, the idea of the Instance Store is not new, but, to the best of our knowledge, it is the first implementation of a general purposed role-free1 DL reasoner over individuals that is sound and complete, has reasonable response times. This claim is supported by presenting a variety of empirical test results [11] contrasting the Instance Store with the other existing techniques such as RACER.
15608177
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
MOTIVATION
We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm.


METHODS
Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples.


RESULTS
The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification.


AVAILABILITY
The method is available at http://dir.niehs.nih.gov/microarray/datamining


CONTACT
LI3@niehs.nih.gov