Sharif Digital Repository

Reconstructing haplotype in MEC (Minimum Error Correction) model is an important clustering problem which focuses on inferring two haplotypes from SNP fragments (Single Nucleotide Polymorphism) containing gaps and errors. Mutated form of human genome is responsible for genetic diseases which mostly occur in SNP sites. In this paper, a fuzzy clustering approach is performed for haplotype reconstruction or haplotype assembly from a given sample Single Nucleotide Polymorphism (SNP). In the best previous approach based on reconstruction rate (Wang 2007[2]), all SNP-fragments are considered with equal values. In our proposed method the value of the fragments are based on the degree of membership... 

Mutations in Single Nucleotide Polymorphisms (SNPs - different variant positions (1%) from human genomes) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies in human genomics. Two sequences of mentioned SNPs in diploid human organisms are called haplotypes. In this paper, we study haplotype reconstruction from SNP-fragments with and without genotype information, problems. Designing serial and parallel classifiers was center of our research. Genetic algorithm and K-means were two components of our approaches. This combination helps us to cover the single classifier's weaknesses. ©2008 IEEE  

Introduction: Proximal spinal muscular atrophy (SMA) is one of the most significant neurodegenerative diseases amongst the autosomal-recessive genetic disorders which is caused by the absence of protein survival of motor neuron (SMN). A critical nucleotide difference in SMN2 compared to SMN1 gene leads to an inefficient protein. Hence, homozygous lack of SMN1 provides a progressive disease. Due to the high prevalence, up to now, several molecular diagnostic methods have been used which most of them are lengthy, expensive, and laborious. Methods: In the present study, we exploited a gold nanoprobe-based method for semi-quantitative SMN1 gene dosage analysis compared to SMN2. The assay was... 

Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repetitive patterns of behavior, interests or activities. Based on the importance of early diagnosis for effective therapeutic intervention, several strategies have been employed for detection of the disorder. The artificial neural network (ANN) as a type of machine learning method is a common strategy. In the current study, we extracted genomic data for 487 ASD patients and 455 healthy individuals. All individuals were genotyped in certain single-nucleotide polymorphisms within retinoic acid-related orphan receptor alpha (RORA), gamma-aminobutyric... 

The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sclerosis (MS) patients and 390 healthy subjects. Single nucleotide polymorphisms (SNPs) within ANRIL (rs1333045, rs1333048, rs4977574 and rs10757278), EVI5 (rs6680578, rs10735781 and rs11810217), ACE (rs4359 and rs1799752), MALAT1 (rs619586 and rs3200401), GAS5 (rs2067079 and rs6790), H19 (rs2839698 and rs217727), NINJ2 (rs11833579 and rs3809263), GRM7 (rs6782011 and rs779867), VLA4 (rs1143676), CBLB (rs12487066) and VEGFA (rs3025039 and... 

Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, Due to the mutated form in human genome; SNPs (Single Nucleotide Polymorphism) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of studies in human genomics. In this paper, a data fusion method based on multiple parallel classifiers for reconstruction of haplotypes from a given sample Single Nucleotide Polymorphism (SNP) is proposed. First, we design a single greedy algorithm for solving haplotype reconstructions. [2] is used as an efficient approach to be combined with first classification method. The... 

Graphene oxide nanowalls with extremely sharp edges and preferred vertical orientation were deposited on a graphite electrode by using electrophoretic deposition in an Mg 2+-GO electrolyte. Using differential pulse voltammetry (DPV), reduced graphene nanowalls (RGNWs) were applied for the first time, in developing an ultra-high-resolution electrochemical biosensor for detection of the four bases of DNA (G, A, T, and C) by monitoring the oxidation signals of the individual nucleotide bases. The extremely enhanced electrochemical reactivity of the four free bases of DNA, single-stranded DNA, and double-stranded DNA (dsDNA) at the surface of the RGNW electrode was compared to electrochemical... 

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic that has been spreading around the world since December 2019. More than 10 million affected cases and more than half a million deaths have been reported so far, while no vaccine is yet available as a treatment. Considering the global healthcare urgency, several techniques, including whole genome sequencing and computed tomography imaging have been employed for diagnosing infected people. Considerable efforts are also directed at detecting and preventing different modes of community transmission. Among them is the rapid detection of virus presence on different surfaces with which people may come in...