Abstract: GRF, Growth regulating factor, genes encode a novel
class of plant-specific transcription factors. The GRF proteins play a
role in the regulation of cell numbers in young and growing tissues
and may act as transcription activations in growth and development
of plants. Identification of GRF genes and their expression are
important in plants to performance of the growth and development of
various organs. In this study, to better understanding the structural
and functional differences of GRFs family, 45 GRF proteins
sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H.
vulgare and S. bicolor, have been collected and analyzed through
bioinformatics data mining. As a result, in secondary structure of
GRFs, the number of alpha helices was more than beta sheets and in
all of them QLQ domains were completely in the biggest alpha helix.
In all GRFs, QLQ and WRC domains were completely protected
except in AtGRF9. These proteins have no trans-membrane domain
and due to have nuclear localization signals act in nuclear and they
are component of unstable proteins in the test tube.
Abstract: Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods.
Abstract: MicroRNAs (miRNAs), a class of approximately 22 nucleotide long non coding RNAs which play critical role in different biological processes. The mature microRNA is usually 19–27 nucleotides long and is derived from a bigger precursor that folds into a flawed stem-loop structure. Mature micro RNAs are involved in many cellular processes that encompass development, proliferation, stress response, apoptosis, and fat metabolism by gene regulation. Resent finding reveals that certain viruses encode their own miRNA that processed by cellular RNAi machinery. In recent research indicate that cellular microRNA can target the genetic material of invading viruses. Cellular microRNA can be used in the virus life cycle; either to up regulate or down regulate viral gene expression Computational tools use in miRNA target prediction has been changing drastically in recent years. Many of the methods have been made available on the web and can be used by experimental researcher and scientist without expert knowledge of bioinformatics. With the development and ease of use of genomic technologies and computational tools in the field of microRNA biology has superior tremendously over the previous decade. This review attempts to give an overview over the genome wide approaches that have allow for the discovery of new miRNAs and development of new miRNA target prediction tools and databases.
Abstract: Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.
Abstract: Gene networks present a graphical view at the level of gene activities and genetic functions and help us to understand complex interactions in a meaningful manner. In the present study, we have analyzed the gene interaction of PPAR-γ (peroxisome proliferator-activated receptor gamma) by search tool for retrieval of interacting genes. We find PPAR-γ is highly networked by genetic interactions with 10 genes: RXRA (retinoid X receptor, alpha), PPARGC1A (peroxisome proliferator-activated receptor gamma, coactivator 1 alpha), NCOA1 (nuclear receptor coactivator 1), NR0B2 (nuclear receptor subfamily 0, group B, member 2), HDAC3 (histone deacetylase 3), MED1 (mediator complex subunit 1), INS (insulin), NCOR2 (nuclear receptor co-repressor 2), PAX8 (paired box 8), ADIPOQ (adiponectin) and it augurs well for the fact that obesity and several other metabolic disorders are inter related.
Abstract: Certain sciences such as physics, chemistry or biology,
have a strong computational aspect and use computing infrastructures
to advance their scientific goals. Often, high performance and/or high
throughput computing infrastructures such as clusters and computational
Grids are applied to satisfy computational needs. In addition,
these sciences are sometimes characterised by scientific collaborations
requiring resource sharing which is typically provided by Grid
approaches. In this article, I discuss Grid computing approaches in
High Energy Physics as well as in bioinformatics and highlight some
of my experience in both scientific domains.
Abstract: Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.
Abstract: HIV-1 genome is highly heterogeneous. Due to this
variation, features of HIV-I genome is in a wide range. For this
reason, the ability to infection of the virus changes depending on
different chemokine receptors. From this point of view, R5 HIV
viruses use CCR5 coreceptor while X4 viruses use CXCR5 and
R5X4 viruses can utilize both coreceptors. Recently, in
Bioinformatics, R5X4 viruses have been studied to classify by using
the experiments on HIV-1 genome.
In this study, R5X4 type of HIV viruses were classified using
Auto Regressive (AR) model through Artificial Neural Networks
(ANNs). The statistical data of R5X4, R5 and X4 viruses was
analyzed by using signal processing methods and ANNs. Accessible
residues of these virus sequences were obtained and modeled by AR
model since the dimension of residues is large and different from
each other. Finally the pre-processed data was used to evolve various
ANN structures for determining R5X4 viruses. Furthermore ROC
analysis was applied to ANNs to show their real performances. The
results indicate that R5X4 viruses successfully classified with high
sensitivity and specificity values training and testing ROC analysis
for RBF, which gives the best performance among ANN structures.
Abstract: Bioinformatics methods for predicting the T cell
coreceptor usage from the array of membrane protein of HIV-1 are
investigated. In this study, we aim to propose an effective prediction
method for dealing with the three-class classification problem of
CXCR4 (X4), CCR5 (R5) and CCR5/CXCR4 (R5X4). We made
efforts in investigating the coreceptor prediction problem as follows: 1)
proposing a feature set of informative physicochemical properties
which is cooperated with SVM to achieve high prediction test
accuracy of 81.48%, compared with the existing method with
accuracy of 70.00%; 2) establishing a large up-to-date data set by
increasing the size from 159 to 1225 sequences to verify the proposed
prediction method where the mean test accuracy is 88.59%, and 3)
analyzing the set of 14 informative physicochemical properties to
further understand the characteristics of HIV-1coreceptors.
Abstract: Classification is an important topic in machine learning
and bioinformatics. Many datasets have been introduced for
classification tasks. A dataset contains multiple features, and the quality of features influences the classification accuracy of the dataset.
The power of classification for each feature differs. In this study, we
suggest the Classification Influence Index (CII) as an indicator of classification power for each feature. CII enables evaluation of the
features in a dataset and improved classification accuracy by transformation of the dataset. By conducting experiments using CII
and the k-nearest neighbor classifier to analyze real datasets, we confirmed that the proposed index provided meaningful improvement
of the classification accuracy.
Abstract: Protein structure determination and prediction has
been a focal research subject in the field of bioinformatics due to the
importance of protein structure in understanding the biological and
chemical activities of organisms. The experimental methods used by
biotechnologists to determine the structures of proteins demand
sophisticated equipment and time. A host of computational methods
are developed to predict the location of secondary structure elements
in proteins for complementing or creating insights into experimental
results. However, prediction accuracies of these methods rarely
exceed 70%.
Abstract: Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.
Abstract: Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.
Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1F88 was
chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. One
group of test data sets that contain total 19 protein sequences was
utilized to access the effect of this method. Compared with the
prediction results of DAS, PRED-TMR2, SOSUI, HMMTOP2.0 and
TMHMM2.0, the obtained results indicate that the presented method
has higher prediction accuracy.
Abstract: Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.
Abstract: Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information. NMRShiftDB is one of the main databases that used to represent the chemical structures in 2D or 3D structures. SMILES format is one of many ways to write a chemical structure in a linear format. In this study we extracted Antimicrobial Structures in SMILES format from NMRShiftDB and stored it in our Local Data Warehouse with its corresponding information. Additionally, we developed a searching tool that would response to user-s query using the JME Editor tool that allows user to draw or edit molecules and converts the drawn structure into SMILES format. We applied Quick Search algorithm to search for Antimicrobial Structures in our Local Data Ware House.
Abstract: This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.
Abstract: The Far From Most Strings Problem (FFMSP) is to obtain a string which is far from as many as possible of a given set of strings. All the input and the output strings are of the same length, and two strings are said to be far if their hamming distance is greater than or equal to a given positive integer. FFMSP belongs to the class of sequences consensus problems which have applications in molecular biology. The problem is NP-hard; it does not admit a constant-ratio approximation either, unless P = NP. Therefore, in addition to exact and approximate algorithms, (meta)heuristic algorithms have been proposed for the problem in recent years. On the other hand, in the recent years, hybrid algorithms have been proposed and successfully used for many hard problems in a variety of domains. In this paper, a new metaheuristic algorithm, called Constructive Beam and Local Search (CBLS), is investigated for the problem, which is a hybridization of constructive beam search and local search algorithms. More specifically, the proposed algorithm consists of two phases, the first phase is to obtain several candidate solutions via the constructive beam search and the second phase is to apply local search to the candidate solutions obtained by the first phase. The best solution found is returned as the final solution to the problem. The proposed algorithm is also similar to memetic algorithms in the sense that both use local search to further improve individual solutions. The CBLS algorithm is compared with the most recent published algorithm for the problem, GRASP, with significantly positive results; the improvement is by order of magnitudes in most cases.