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Functional region identification is of fundamental importance for protein sequences analysis for a protein family. Such knowledge not only provides a better scientific understanding but also assists drug discovery. Domain annotation is one approach but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally similar sub-sequences and represents them...
Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical...
Discovering protein patterns for amino acids and their biochemical properties is important for revealing the underlying biophysical models. From this, pattern clustering was introduced in order to relate the discovered protein patterns to taxonomic classes in a localized region of a protein. This paper proposes an algorithm to synthesize and re-group pattern clusters, maximizing their separability...
Clustering homologous proteins is one of the important tasks in functional genomics. Homologous proteins may share common functions. Annotating proteins of unknown function by transferring annotations from their homologues of known annotations is one of the efficient ways to predict protein function. We use a modularity-based method called CD for grouping together homologous proteins. The method employs...
Remote homology detection among proteins in an unsupervised approach from sequences is an important problem in computational biology. The existing neighborhood cluster kernel methods and Markov clustering algorithms are most efficient for homolog detection. Yet they deviate from random walks with inflation or similarity depending on hard thresholds. Our spectral clustering approach with new combined...
Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein-protein interaction network structure and gene co-expression relationship into...
This paper presents a novel priority based data mining algorithm using improved K-means clustering for detecting proteins sequence from dataset of frequent item set. The priorities are set depending on the number of hits (counts) from the dataset concurrently using the concept of multiprocessing. Which dynamically changing for a period of time series, a novel algorithm is used for classification and...
Recently, liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technique for identifying differential abundance of peaks as biomarkers. Two major problems in the preprocessing of LC-MS data analysis are how to adjust and align multiple LC-MS datasets efficiently and correctly. Hence, an effective algorithm is needed to adjust the variation in retention time and align protein...
In proteomics studies, liquid chromatography coupled to mass spectrometry (LC-MS) has proven to be a powerful technology to investigate differential expression of proteins/peptides that are characterized by their peak intensities, mass-to-charge ratio (m/z), and retention time (RT). The variable complexity of peptide mixtures and occasional drifts leads to substantial variations in m/z and RT dimensions...
MS/MS experiments generate hundreds to tens of thousands of fragment ion spectra during the experiment. In the peptide identification, MS/MS spectra are often identified by database searching algorithms such as SEQUEST and Mascot. Most database searching algorithms calculate score functions to compare the experimental MS/MS spectra with theoretical MS/MS spectra of certain peptides derived from protein...
Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are some subsequences which appear frequently. Patterns often denote important functional regions in proteins and can be used to characterize a protein family or discover the function of proteins. Moreover, it provides valuable information...
The massively parallel computing using graphical processing unit (GPU), which based on tens of thousands of parallel threats within hundreds of GPU's streaming processors, has gained broad popularity and attracted researchers in a wide range of application areas from finance, computer aided engineering, computational fluid dynamics, game physics, numerics, science, medical imaging, life science, and...
Most quantitative cell image-based screening analyses are dependent on thorough user supervision based on assay-specific knowledge. To minimize human bias in analysis, we introduce an automated methodology of displaying screen phenotypes using clustering that provides intuitive visuals to guide user supervision when required. Our premise is to automatically present to users an overview of screen phenotype-contents...
NSF1 is one of the newly discovered fermentation stress response proteins that play a crucial role in the adaptation of the yeast Saccharomyces cerevisiae to fermentation stress conditions. Using time course microarray gene expression profiles of Saccharomyces cerevisiae (DBY7286) grown in YPD media, we identified and mapped genes significantly correlated to the NSF1 expression, hence producing a...
Relational databases provide unprecedented opportunities for knowledge discovery. Various approaches have been proposed to infer structures over entity types and predict relationships among elements of these types. However, discovering structures beyond the entity type level, e.g. clustering over relation concepts, remains a challenging task. We present a Bayesian nonparametric model for joint relation...
This paper proposes a local linear multi-SVM method based on composite kernel for solving classification tasks in gene function prediction. The proposed method realizes a nonlinear separating boundary by estimating a series of piecewise linear boundaries. Firstly, according to the distribution information of training data, a guided partitioning approach composed of separating boundary detection and...
Understanding the genetic factors that promote recombinant protein accumulation in transgenic plants will provide insightful strategies for protein biofactory efficiency. Through combining biological and bioinformatics analysis, our work is to determine genetic and biological factors affecting increased protein accumulation of a bacterial cellulase enzyme in transgenic maize. Microarray experiments...
Community structure or clustering is ubiquitous in many evolutionary networks including social networks, biological networks and financial market networks. Detecting and tracking community deviations in evolutionary networks can uncover important and interesting behaviors that are latent if we ignore the dynamic information. In biological networks, for example, a small variation in a gene community...
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