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G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein–protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug–target interactions. [ABSTRACT FROM AUTHOR]

Background: Multiple sclerosis (MS) is a common cause of neurologic disability in young adults. Individuals with MS deal with the day-to-day effects of the disease on their lives. Self-management can help with these challenges. This study aimed to explore MS self-management needs according to experiences of persons with MS and was conducted as part of a research project to develop an MS selfmanagement mobile application. Methods: We used a qualitative method to elicit self-management needs among 12 individuals with MS and conducted semistructured interviews with them. The participants were chosen based on snowball sampling. The interviews were recorded and transcribed verbatim. Finally, qualitative data were analyzed using a content analysis method (inductive way) to identify the underlying themes and subthemes. Results: The analysis resulted in the emergence of 7 themes: the source of information, basic needs, understanding MS, physical exercises in MS, useful nutrition in MS, MS monitoring, and communication. Within these 7 themes we identified 23 subthemes. Conclusions: The themes that emerged in this study show what needs are essential to help persons with MS improve their self-management capacity. These findings can help in the development of self-management mobile applications for supporting individuals in managing MS. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted. [ABSTRACT FROM AUTHOR]

Introduction: Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set. Material and Methods: Several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchical clustering algorithm (HCM) were applied using orange version 3.22 as an open source data mining tool. Results: The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics. Conclusion: These finding provide the employed algorithms in this study can be helpful to our aim. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle. [ABSTRACT FROM AUTHOR]

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs. [ABSTRACT FROM AUTHOR]

Background: Mobile applications are increasingly being used to support nutrition improvement. They could be effectively used for dietary intake assessment, weight management, physical activity monitoring, nutritional behaviors improvement, and nutrition education. Objective: The aim of this study was to investigate current nutrition-related mobile apps on the Iranian apps store (Bazaar) and review their characteristics. Material and Methods: This descriptive cross-sectional study was conducted on all Iranian mobile applications related to nutrition OR diet in 2022. Bazaar was used to search for nutrition-related applications using the following keywords: "diet", "nutrition", and their Persian translations. For each app, the extracted information was name, requirement for Internet connectivity, estimated number of downloads, user satisfaction rate, developers, and the main features of each app. Data collected from application description and application developer's webpage. Results: 76 mobile apps were identified from Bazaar app store. About 21% of them were developed by personal developers and 10 apps did not mention any specific developer. 42 applications were online and 33 applications were offline. Only 1 app could be used online/offline. In user satisfaction rate, 47 apps had 4-4.9 rating, and 7 apps had a rating of 5. Only 2 apps had under 3 rating. Conclusion: Iranian apps on nutrition and diet are widely available and very popular but currently most of them lack professional and evidence-based content. Developing apps based on standard and evidence-based online approaches would assure content quality, allowing healthcare professionals to recommend their use by people. [ABSTRACT FROM AUTHOR]

Background: Smartphones with a set of embedded sensors provide a practical data collection opportunity to explore new modalities of monitoring, and research of psychiatric and mental health conditions. Through utilizing mobile sensing, different features and patterns can be monitored to get more insight into different behavioral and contextual aspects of depression. Objective: This study aimed to investigate any use of mobile sensing for monitoring different parameters and features in depression. Material and Methods: A comprehensive literature review was conducted in different data bases including Scopus, PubMed, ProQuest, IEEE, EMBASE, web of science, and Cochrane. Inclusion criteria were defined for the studies. To be included in our review, studies must have been report usage of mobile sensing among patients with depression. Peer-reviewed journal papers and conference proceedings papers were included; extended abstracts were excluded. Results: A total number of 44 studies met the criteria. The most frequently used mobile sensing were GPS(n=41), phone usage(n=33), and accelerometer(n=23) followed by Wi-Fi(n=10), Microphone(n=9), Light(n=8), Bluetooth(n=5), proximity(n=2), gyroscope(n=1), and temperature(n=1) sensors. The main categories of features monitored by mobile sensing included: geographical position, activity/moving duration/time, sleep state and pattern, social interaction, Number and pattern of photos taken by mobile camera, phone usage pattern, home stay or being out of home and environmental state/context. Association between depression status and some features monitored through mobile sensing had been reported in 15 studies. Conclusion: Mobile sensing has been used in different studies for monitoring different behavioral and contextual features and patterns among patients with depression. However it appears that more randomized clinical trial studies are needed to conclude about the association between features monitored through mobile sensing and depression status. [ABSTRACT FROM AUTHOR]