This paper gives a detailed review of current techniques in quantification, however the methods discussed, have different levels of sophistication in both theory and practice, and the level of sophistication does not necessarily correlate with the effectiveness of the method. Meanwhile, each method has different capability to deal with various challenges including the ever-increasing density of spot layout, irregular shapes of spots, and inevitable contaminations. Clearly the proposed method provides a need, for improving the consistency, robustness, and accuracy of the microarray image quantification I. INTRUDUCTION Microarray sequences on a single microscopic glass slide. The extraction of gene expression levels is accomplished through image analysis techniques namely gridding, spot segmentation and intensity extraction. allows the simultaneous study of tens of thousands of different DNA nucleotide Extracted mean intensities correspond to gene expression levels that, in turn, are translated into biological conclusions by molecular biologists, using data mining techniques. However, microarray experiments involve a number of error prone steps (occurring during fabrication, target labeling, and hybridization), which induce noise on the resulting images. Microarray images are also corrupted by irregularities in shape, size, and position of the spot. The ultimate goal in microarray image analysis is to automatically quantify each spot, giving relevant extent of hybrizations of the two samples, a process known as quantification. II.PROPOSED METHOD An efficient quantification algorithm is proposed to validate the performance of segmentation. We demonstrate the success of the proposed method by measuring confidence interval of the propo... ... middle of paper ... ... 2.36]. The lengths of 95% confidence interval are: [3.35, 4.18, 4.18, 2.82] The means of added and deleted pixels for various spots in a simulated image are: [8.40, 6.19, 5.15, 1.48] and standard deviation are: [17.96, 8.19, 5.31, 0.38]. The confidence intervals for the GMM, K-means, Multifeature and proposed are: [c1, c2]: [6.76, 10.04], [5.44, 6.93], [4.66, 5.63], [1.44, 1.51]. The lengths of 90% confidence interval are: [3.28, 1.49, 0.97, 0.07]. The lengths of 95% confidence interval are: [3.91, 1.78, 1.16, 0.08]. Length of 90% confidence intervals for sample mean of foreground in each case; Length of 95% confidence intervals for sample mean of foreground pixels in each case Length of 90% Confidence intervals for sample mean of added and dropped foreground in each case Length of 95% Confidence intervals for sample mean of added and dropped foreground in each case
For both experiments, data were collected for thirty seconds.
The thresholds used to calculate each mean were not highly variable between trials. The data recorded over each trial were highly consistent between one another, except for a slight deviation in the measurements recorded on the palm of the hand. During ascending trial three on the palm, the results deviated from the norm in reference two the two prior trails. On trial one and two, 0.05 was the only measurement that was not felt. On trial three, not only was 0.05 not felt, but 0.10 was also not felt, which deviated from the norm set forth in the two prior
For this statistical inference, the question was whether the means were truly different or could they have been samples from the same population. To do draw a conclusion, we must first assume normal distribution. We must also set the null hypothesis to m1 - m2 = 0. And per this assignment we must set the a-level at .05 and the hypothesis alternative to m1 - m2 ¹ 0; thus requiring a two-tailed test.
This method is used since it is the most appropriate for calculating the mean and the standard deviation of a grouped data.
The epigenome marks the genome, determining whether or not a gene is expressed and if so, to what level. It does this in two ways, DNA methylation, and histone modification. DNA methylation is where a methyl group, a tag of carbon and hydrogen, connects to a part of DNA (to the gene) and decides for it to be expressed or not. Histone modification is where a chemical tag secures a histone, or a protein, and tightens or loosens the gene's coil around it to determine how greatly the gene is expressed.
Proteogenomics is a kind of science field that includes proteomics and genomics. Proteomic consists of protein sequence information and genomic consists of genome sequence information. It is used to annotate whole genome and protein coding genes. Proteomic data provides genome analysis by showing genome annotation and using of peptides that is gained from expressed proteins and it can be used to correct coding regions.Identities of protein coding regions in terms of function and sequence is more important than nucleotide sequences because protein coding genes have more function in a cell than other nucleotide sequences. Genome annotation process includes all experimental and computational stages.These stages can be identification of a gene ,function and structure of a gene and coding region locations.To carry out these processes, ab initio gene prediction methods can be used to predict exon and splice sites. Annotation of protein coding genes is very time consuming process ,therefore gene prediction methods are used for genome annotations. Some web site programs provides these genome annotations such as NCBI and Ensembl. These tools shows sequenced genomes and gives more accurate gene annotations. However, these tools may not explain the presence of a protein. Main idea of proteogenomic methods is to identify peptides in samples by using these tools and also with the help of mass spectrometry.Mass spectrometry searches translation of genome sequences rather than protein database searching. This method also annotate protein protein interactions.MS/MS data searching against translation of genome can determine and identify peptide sequences.Thus genome data can be understood by using genomic and transcriptomic information with this proteogenomic methods and tools. Many of proteomic information can be achieved by gene prediction algorithms, cDNA sequences and comparative genomics. Large proteomic datasets can be gained by peptide mass spectrophotometry for proteogenomics because it uses proteomic data to annotate genome. If there is genome sequence data for an organism or closely related genomes are present,proteogenomic tools can be used. Gained proteogenomic data provides comparing of these data between many related species and shows homology relationships among many species proteins to make annotations with high accuracy.From these studies, proteogenomic data demonstrates frame shifts regions, gene start sites and exon and intron boundaries , alternative splicing sites and its detection , proteolytic sites that is found in proteins, prediction of genes and post translational modification sites for protein.
...bioanalytical systems which based on electrochemiluminescence detection and evanescent field fluorescent detection showed the best sensitivities (Dupuy et al., 2009). The power of immunoblotting is extended with this method in order to provide a quantitative analysis of differential expression of active and parental proteins (Tibes et al., 2006). Moreover, by using RPPA, samples can be spotted at same time which is suitable for retrospective analysis of large number of specimens. This technique which can be used to analyze large number of proteins from each sample is suitable to analyze the population of cells that present in low numbers. On the other hand, this technique also has limitation which it only identifies to known proteins or targets only (Tibes et al., 2006). However, it stills a useful technique that use in the study of functional proteomics analysis.
du Prel, J. B., Hommel, G., Rohrig, B., & Blettner, M. (2009). Confidence interval or p-value?
Nevertheless, functional genomics is an area of research which has been widely developed due to microarray technology; providing a wide-scale platform for the analysis of genes.
The proposed multimodel segmentation was tested with almost all combination of mass shapes and margins in CC and MLO views and the segmented abnormal region was verified with ground truth table images in which abnormality marked by radiologist in the DDSM database. Further feature extraction methods and classifier has to be developed for fully automated diagnosis CAD system. Further study has to be carried out to test the algorithm for the segmentation of micro calcifications.
The Variable reference using a third set of samples would create a greater range of data to work with and would create a slightly more accurate mean calculation.
Freeman, S. (2014). HowStuffWorks "How DNA Profiling Works". [online] Retrieved from: http://science.howstuffworks.com/dna-profiling.htm [Accessed: 31 Mar 2014].
Linear Discriminant Analysis (LDA), also known as Fisherface method, uses the Fisher’s linear discriminant criterion to overcome the limitations of eigenfaces method (Batagelj, 2006). This criterion tries to maximize the ratio of the determinant of the between-class scatter matrix of the projected samples to the determinant of the within-class scatter matrix of the projected samples. The aim is to maximize the between-class scatter while minimizing the within-class scatter.
Minutiae-based techniques: In these minutiae points are finding and then mapped to their relative position on finger. There are some difficulties like if image is of low quality it is difficult to find minutiae points correctly also it considers local position of ridges and furrows not global [4].
DNA fingerprinting, also known as DNA typing, is the analysis of DNA (deoxyribonucleic acid) samples through isolation and separation. This technique of identification is called “fingerprinting” because, like an actual fingerprint, it is very unlikely that anyone else in the world will have the same pattern. Only a small sample of cells is required to preform a successful DNA fingerprint. The root of a hair, a single drop of blood, or a few skin cells is enough for DNA testing. DNA fingerprinting has many uses, some of which include crime scene investigations and paternity cases.