Browsing by Author "Mohamed Abdelhafid Hamidechi"

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    Changes in bacterial populations in refrigerated raw milk collected from a semi-arid area of Algeria
    (Springer, 2015-10-14) Mohamed Abdelhafid Hamidechi
    Abstract Most of the studies on milk microbiota have been performed on cows’ milk from animals reared in temperate and humid areas. In this work, changes in the bacterial consortium of refrigerated raw milk collected from cows grazed in a semi-arid area of Algeria were studied during 21 days of refrigerated storage. Twenty bacterial morpho-physiotypes were selected among 150 isolates from milk at different times over storage and identified by partial 16S rRNA gene sequencing. The dominant bacterial populations were characterized by a few species. Stenotrophomonas rhizophila, S. maltophilia and Chryseobacterium indologenes were predominant during the first 7 days, Lactobacillus pentosus and L. plantarum were isolated only after the 10th day, while Acinetobacter spp. was isolated at the end of storage. Compared to the current literature on milk from temperate zones, sluggish and incomplete microbial growth was observed with a long incubation phase ranging from 6.7 to 10.5 days and a maximum growth not exceeding 5.3 log colony-forming units (CFU)·mL−1. The composition of milk microbiota and its evolution over refrigeration suggest a biogeographical characterization due to environmental factors. In particular, the possible presence of antimicrobial molecules coming from plants grazed in the semi-arid zone around the farm may account for the presence of selected microbial species and the extended milk shelf-life. Despite this being a preliminary work, these results encourage the use of arid herbs in animal feed and motivate scientists to focus their efforts on the study of biochemical composition of plants from arid areas and their antimicrobial activity
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    DeepEnz: prediction of enzyme classification by deep learning
    (Indonesian Journal of Electrical Engineering and Computer Science, 2021-03-30) Mohamed Abdelhafid Hamidechi
    Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification
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    EZYDeep: A Deep Learning Tool for Enzyme Function Prediction based on Sequence Information
    (The Open Bioinformatics Journal, 2023-06-07) Mohamed Abdelhafid Hamidechi
    Abstract: Introduction: Enzymes play a crucial role in numerous chemical processes that are essential for life. Accurate prediction and classification of enzymes are crucial for bioindustrial and biomedical applications. Methods: In this study, we present EZYDeep, a deep learning tool based on convolutional neural networks, for classifying enzymes based on their sequence information. The tool was evaluated against two existing methods, HECNet and DEEPre, on the HECNet July 2019 dataset, and showed exceptional performance with accuracy rates over 95% at all four levels of prediction. Results: Additionally, our tool was compared to state-of-the-art enzyme function prediction tools and demonstrated superior performance at all levels of prediction. We also developed a user-friendly web application for the tool, making it easily accessible to researchers and practitioners. Conclusion: Our work demonstrates the potential of using machine learning techniques for accurate and efficient enzyme classification, highlighting the significance of sequence information in predicting enzyme functionb
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    RNA 2D Structure Prediction: A review
    (Sciences & Technologie, 2016-10-20) Mohamed Abdelhafid Hamidechi
    RNA, a macromolecule that provides several biological functions: gene translation into proteins, regulation of gene expression, prediction of 3D structure and RNA function, etc. In this work, we will review the prediction of RNA secondary structures by dynamic programming based on the classical Nussinov algorithm. We took into account four possible links between the nucleotides forming the RNA polymer chain (canonical GC, CG, AU AU bonds, wobble bonds: GU or UG, etc.). The program tested in this work shows that the developed algorithm correctly predicts the different base pairs that enter the 2D structure of the RNA