RESUMO
Background: Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes. Methods: In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs. Results: The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively. Conclusions: The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.
RESUMO
Up to now, various signal processing techniques have been used to predict protein-coding genes that are unsuitable for predicting ribonucleic acids (RNAs). Modeling a gene network can be employed in various fields, such as the discovery of new drugs, reducing the side effects of treatment methods, further identifying genetic diseases and treatments for genetic disorders by influencing the activity of effectual genes, preventing the growth of unwanted tissues via growth weakening and cell reproduction, and also for many other applications in the fields of medicine and agriculture. The main purpose of this study was to design a suitable algorithm based on context-sensitive hidden Markov models (csHMMs) for the alignment of secondary structures of RNAs, which can identify noncoding RNAs. In this model, several RNA families are compared, and their existing similarities are measured. An expectation-maximization algorithm is used to estimate the model's parameters. This algorithm is the standard algorithm to maximize HMM parameters. The alignment results for RNAs belonging to the hepatitis delta virus family showed an accuracy of 83.33%, a specificity of 89%, and a sensitivity of 97%, and RNAs belonging to the purine family showed an accuracy of 65%, a specificity of 76%, and a sensitivity of 76%. The results show that csHMMs, in addition to aligning the primary sequences of RNAs, would align the secondary structures of RNAs with high accuracy.
