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High throughput genomic techniques produce datasets involving thousands of gene expression profiles. In order to infer biologically meaningful regulatory interactions, a dimensionality reduction must take place to identify genes or groups of genes that are important to the biological system being analyzed. Here we provide a systematic approach to remove dispersible genes from consideration based on...
This study presents a strategy for reducing the effects of noise on the location of RNA motifs in the context of a previously developed analysis pipeline. The pipeline was developed to search for novel RNA motifs incorporating both primary and secondary structure. The ability of the pipeline to detect motifs in the presence of a relatively large amount of sequence not containing a target motif is...
In solving the question of alignment of two RNA secondary structures, it is not clear how to choose parameters for the introduction of gaps in the alignment. We introduce a Bayesian approach for aligning two RNA secondary structures. The algorithm can not only compute the optimal alignment itself but also some interesting posteriors such as posteriors of substitution matrix and gap opening penalties...
This paper explores the use of the fuzzy C-means clustering algorithm for inferring functional relationships among proteins from their phylogenetic profiles. We conducted a series of experiments using phylogenetic profiles provided by the Cluster of Orthologous Groups of Proteins (COG) database. Experimental results demonstrate that the increasing expressiveness of fuzzy C-means enables the discovery...
Classifying G protein-coupled receptors (GPCRs) is an interesting topic because of the important role of GPCRs in pharmaceutical research. GPCRs have diverse functions and are involved in many biological processes, which makes them an ideal target of novel medicine. The diverse nature of GPCRs results in the lack of overall sequence homolog among the members, making the classification of GPCRs a very...
Successful biological image analysis usually requires satisfactory segmentations to identify regions of interest as an intermediate step. Here we present a novel graphical model approach for segmentation of multi-cell yeast images acquired by fluorescence microscopy. Yeast cells are often clustered together, so they are hard to segment by conventional techniques. Our approach assumes that two parallel...
Random environment of the cell demands for a stochastic tool to analyze its behavior and bring the aspects of the bio-process that has been shadowed by the kinetic rate law based approach. In this work we present a novel random walk approach to detect the dynamics of passive diffusion of a metabolite in the gram-negative bacteria through phosphoenolpyruvate phospho-transfer system (pts pathway). We...
In a gene expression data matrix a bicluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The premise behind biclustering is that even related genes may only be expressed in a synchronized fashion over certain conditions. Conventional clustering groups over all features and may not capture these local relationships. Biclustering...
Aiming at the two main shortcomings in Homology Modeling, we have designed and established a domain clustering database. Searching the database is a fundamental work for it. However, current alignment algorithms are mainly based on the sequences, ignoring the structure conservation in domain. This paper proposed a profile-based alignment which considers the structure information into the profile,...
A number of biclustering approaches have been developed to mitigate the limitations of standard clustering algorithms. They have different problem formulation, search strategy and computational complexity. We proposed a new biclustering method based on the framework of market basket analysis in which a bicluster is described as a frequent itemset. As a feasibility test, we compared it with several...
The DNA microarray technology, originally developed to measure the level of gene expression, had become one of the most widely used tools in genomic study. Microarrays have been proved to benefit areas including gene discovery, disease diagnosis, and multi-virus discovery. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences...
One of the central problems in computational biology is to identify the protein function in an automated and high-throughput fashion. A key step in this process is to predict subcellular compartment the protein belongs to, since the protein localization closely correlates with its function. A wide variety of methods for protein subcellular localization has been proposed over recent years. They fall...
Sequencing technologies used to generate long strands of DNA are susceptible to laboratory errors that may result in several DNA nucleotides being deleted from the genome. Detecting such deletions in the protein coding regions is of utmost importance. Missing even a single nucleotide may lead to frame shifts with all the following codons (and consequently the encoded amino acids) being identified...
In this paper, we propose a new method for inferring functional relationships between proteins from their phylogenetic profiles. The evolutionary bond energy algorithm (EBEA) enhances the greedy BEA clustering algorithm with genetic search. We conducted a series of experiments using phylogenetic profiles provided by the Cluster of Orthologous Groups of Proteins (COG) database. Experimental results...
This work was intended as an attempt to more global approach to the issue dealing with heuristic image matching (more complex images registration and landmarks arrangement). Proposed methodology is suitable for biomedical images congruencing of high-resolution images and large images size. The 2-D filter suitable to image features detection using reformulated stretched-Gabor wavelet has been presented...
Quantum mechanics and molecular dynamic simulation provide important insights into structural configurations and molecular interaction data today. To extend this atomic/molecular level capability to system level understanding, we propose an "in silico" stochastic event based simulation technique. This simulation characterizes the time domain events as random variables represented by probabilities...
Computational prediction of transcription factor's binding sites and regulatory target genes has great value to the biological studies of cellular process. Existing practices either look into first-hand gene expression data which could be costly for large scale analysis, or apply statistical or heuristic learning methods to discover potential binding sites which have limited accuracy due to the complexity...
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