Ahmed Zohair Ibrahim^1,*, P. Prakash², V. Sakthivel², P. Prabu³

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2447-2460, 2023, DOI:10.32604/csse.2023.030134

Abstract In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brainfunction is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer’s disease, Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in… More >

Muhammad Hamraz¹, Dost Muhammad Khan¹, Naz Gul¹, Amjad Ali¹, Zardad Khan¹, Shafiq Ahmad², Mejdal Alqahtani², Akber Abid Gardezi³, Muhammad Shafiq^4,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3663-3677, 2023, DOI:10.32604/cmc.2023.030064

Abstract The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the “Median” as well as the measures of variation i.e., “Median absolute deviation (MAD) and Interquartile range (IQR)” in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR). The results obtained via… More >

Ala Saleh Alluhaidan^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5949-5962, 2022, DOI:10.32604/cmc.2022.022028

Abstract Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the… More >

MARGARET SIMONIAN^*

BIOCELL, Vol.45, No.6, pp. 1459-1463, 2021, DOI:10.32604/biocell.2021.017369

Abstract Pediatric central nervous system tumors are the most common tumors in children, it constitute 15%–20% of all malignancies in children and are the leading cause of cancer related deaths in children. Proteogenomics is an emerging field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of biomarkers for diagnosis and therapeutic purposes. Integrative proteogenomics analysis of pediatric tumors identified underlying biological processes and potential treatments as well as the functional effects of somatic mutations and copy number variation driving tumorigenesis. More >

Hernández-León R¹, M Martínez-Trujillo², E Valencia-Cantero¹, G Santoyo¹

Phyton-International Journal of Experimental Botany, Vol.81, pp. 133-137, 2012, DOI:10.32604/phyton.2012.81.133

Abstract Rhizospheric soil of wheat plants contains a high diversity of microorganisms, and therefore, comprises a large reservoir for discovering genes with diverse agro-biotechnological applications. In this work, we constructed an E. coli metagenomic library based on bacterial artificial chromosome (BAC) clones with large genomic inserts from metagenomic DNA from the rhizosphere of wheat plants. The average of the DNA cloned segments varies from 5 to 80 kb, with an average size of 38 kb. Random clones were end-sequenced and homology results showed that the clonation of metagenomic DNA codes mainly for metabolic and catalytic functions (40%), including amidohydrolase, hydrolase, peptidase,… More >

Rojas-Herrera R¹, M Zamudio-Maya¹, L Arena-Ortiz², RC Pless³, A O’Connor-Sánchez⁴

Phyton-International Journal of Experimental Botany, Vol.80, pp. 231-240, 2011, DOI:10.32604/phyton.2011.80.231

Abstract Mexico counts among the five countries with the highest biodiversity in the world. In the Yucatán Peninsula, there are aquatic ecosystems with a very special microbial diversity. These ecosystems are essential for the ecological equilibrium of the region, and are seriously threatened by human activities. Access and knowledge of the microbial resources of these environments have an enormous scientific interest, and could potentially result in biotechnological products which could lead to more efficient and environmentally friendly processes; it could also offer a full arsenal of microorganisms and/or novel molecules to the local and world industry to face the current needs.… More >

Hernández-León R, I Velázquez-Sepúlveda, MC Orozco-Mosqueda, G Santoyo

Phyton-International Journal of Experimental Botany, Vol.79, pp. 133-139, 2010, DOI:10.32604/phyton.2010.79.133

Abstract Soil is a complex system that includes a great number and diversity of microorganisms. Until recently, only a small percentage of the bioma was known and could be studied. Currently, it is possible to have a deeper knowledge of all that unknown genomic material with the development of new tools, like metagenomics. New molecules have been discovered with various biotechnological applications, and knowledge of the diverse microbiological interactions in several environments, some of them with extreme life conditions, is much higher. We analyze the most recent literature in the field of metagenomics in this study, especially that related with the… More >