Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.

The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.

Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning.

Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and social viewpoint. Indeed, timely diagnosing through different medical examinations like anthropometric measurements of the skull or even Computer Tomography (CT) image modality followed by a periodical screening and monitoring plays a vital role in treatment phase. In this paper, a classification model for detecting and monitoring deformational plagiocephaly in affected infants is presented. The presented model is based on a deep learning network architecture. The given model achieves high accuracy of 99.01% with other classification parameters. The input to the model are the images captured by commonly used smartphone cameras which waives the requirement to sophisticated medical imaging modalities. The method is deployed into a mobile application which enables the parents/caregivers and non-clinical experts to monitor and report the treatment progress at home.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey.

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Thymus daenensis extract and essential oils effects on morphine withdrawal signs in mice.

BACKGROUND: Thymus species are well known medicinal plants which the previous studies suggested the involvement of the opioid system in them. OBJECTIVES: This study aimed to investigate the effects of methanolic extract and essential oil of aerial parts of Thymus daenensis (TD), an endemic aromatic medicinal plant of Iran, on morphine withdrawal syndrome in mice. MATERIALS AND METHODS: Experiments were performed in two groups of five, each group treated with extracts or essential oils of TD. Dependency was induced by subcutaneous injection of morphine for three consecutive days. On the fourth day, the last dose of morphine was injected two hours prior to intraperitoneal injection of naloxone while the extract or essential oil of TD was administered 30 minutes before naloxone. A period of 20 minutes after naloxone injection was considered the critical period of the withdrawal syndrome. The number of jumps, standing, leaning, and the weight of stools were recorded as withdrawal signs. RESULTS: The 200 mg/kg and 400 mg/kg doses of extract and all doses of essential oil decreased significantly the number of jumps, standing, leaning and the weight of stool. Administration of 100 mg/kg of extract only decreased the weight of stool and had no effect on the other factors. CONCLUSIONS: Extract and essential oil of TD attenuates morphine withdrawal behaviors in mice and may be useful in alleviating the signs and symptoms of opiate withdrawal syndrome in human.