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A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences. Main idea behind multiple sequence alignment is to see the similarities between input sequences, to be able to make phylogenetic analysis and other evolutionary conclusions. We propose a multiple sequence alignment method based on contact maps derived from structural data and network properties. We show...
RNA interference (RNAi) is the mechanism through which RNA interferes with the production of other RNAs in a sequence specific manner. Micro RNA (miRNA) is a type of RNA which is transcribed as pri-miRNAs and processed to pre-miRNAs in the nucleus. These pre-miRNAs are then exported from the nucleus and processed in the cytoplasm to double stranded RNA with one strand providing target specificity...
There are many advances in nurse-call devices but there is a lack of proper integration and interoperation among them. This paper presents a software application to join these devices together in a LAN environment such as Hospital Information System. This software application is called Supervising Event Executive (SEE) that is developed to provide a common, convenient, and reliable means of transferring...
Distribution of electrical potentials over the surface of the heart, which is called the epicardial potential distribution, is a valuable tool to understand whether there is a defect in the heart. Direct measurement of these potentials requires highly invasive procedures. An alternative is to reconstruct these epicardial potentials non-invasively from the body surface potentials, which constitutes...
The present study analyzes brain hemodynamic concentration of frontal cortex during four cognitive mental tasks. The analysis procedure consists of three sequential steps. First, the strong brain activation regions have been investigated thoroughly from all subjects in order to find a proper electrode location that generates important brain stimuli. Second, a feature extraction method that is based...
In this work we have developed a new approach to identify different types of electrocardiogram (ECG) beats using discrete wavelet transform (DWT) coefficients. The purpose of the study is to develop a simple algorithm for the diagnosis of some cardiac abnormalities. Five types of cardiac phenomena are considered and for each of these some particular records from the MIT-BIH Arrhythmia Database are...
In this paper, we present a domain specific search engine that relies on extracting the semantic relation among medical documents. Our goal is to maximize the contextual retrieval and ranking performance with minimum input from users. We have performed experiments to measure the effectiveness of the proposed technique by evaluating the performance of the retrieval process in terms of recall, precision...
Phylogenic analysis relies on alignment of related sequences from different species to obtain the distances between these species. The quality of the alignment and the distance measure would depend on the alignment parameters that are used. In this work, we propose to use Relative Complexity Measure (RCM) to find the distances between the sequences which is not a parameter dependent measure. We used...
The following topics are dealt with: decision support systems; computer aided diagnosis; sequence analysis; phylogenetics; clinical informatics; hospital information systems; structural bioinformatics; machine learning; medical signal analysis; medical image analysis; medical signal visualization; medical image visualization; genome-wide bioinformatics; systems biology.
The coupling between energy fluctuations and positional fluctuations in molecular dynamics trajectories of Crambin at 310 K is studied. Coupling with energy fluctuation is evaluated for both atomic positions and residue positions. Couplings show values which fluctuate around the previously proposed theoretical value under harmonic approximation. The magnitude of these correlations is in agreement,...
This article addresses one of a wide range of computer aided detection/diagnosis (CAD) problems. Specifically, our aim is to detect the relatively common spina bifida pathology in the prenatal period exploiting fetal ultrasound images. Among many potential ways to perform such detections, we work with transcerebellar brain images of the fetus and search for the presence of an indicative marker of...
2D or 3D dataset transformations, interpolations and resampling operations are essential techniques that are able to display graphical models in several fields such as medical visualization and engineering. In this study, an overview of interpolation approaches of the last ten years was given and a resampling application for magnetic resonance images was implemented using free form cubic b-spline...
Protein interaction domains play crucial roles in many complex cellular pathways. PDZ domains are one of the most common protein interaction domains. Prediction of binding specificity of PDZ domains by a computational manner could eliminate unnecessary, time-consuming experiments. In this study, interactions of PDZ domains are predicted by using a machine learning approach in which only primary sequences...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
There are different kinds of tumors in childhood: nephroblastoma, clear cell sarcoma, neuroblastoma etc. For diagnosis MRI (Magnetic resonance images) are used. Our research is the first mathematical approach on MRI (Magnetic resonance images) of renal tumors. We are using transversal, frontal and sagittal images and compare them in their potential for differentiation different kind of tumors. The...
Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray data. In this work, we aim to develop an automated system for robust and reliable cancer diagnoses based on gene microarray data. Support vector machine classifiers outperform other popular classifiers, such as K nearest neighbours, naive Bayes, neural networks and decision tree, often to a remarkable...
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