HormoNet: a deep learning approach for hormone-drug interaction prediction.

Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.

In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy.

Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.

Analysis and identification of drug similarity through drug side effects and indications data.

BACKGROUND: The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects. METHODS: The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity. RESULTS: Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities. CONCLUSION: Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.

New approaches in developing medicinal herbs databases.

Medicinal herbs databases have become a crucial part of organizing new scientific literature generated in medicinal herbs field, as well as new drug discoveries in the information era. The aim of this review was to track the current status of medicinal herbs databases. Search for finding medicinal herbs databases was carried out via Google and PubMed. PubMed was searched for papers introducing medicinal herbs databases by the recruited search strategy. Papers with an active database on the web were included in the review. Google was also searched for medicinal herbs databases. Both retrieved papers and databases were reviewed by the authors. In this review, the current status of 25 medicinal herbs databases was reviewed, and the important characteristics of databases were mentioned. The reviewed databases had a great variety in terms of characteristics and functions. Finally, some recommendations for the efficient development of medicinal herbs databases were suggested. Although contemporary medicinal herbs databases represent much useful information, adding some features to these databases could assist them to have better functionality. This work may not cover all the necessary information, but we hope that our review can provide readers with fundamental concepts, perspectives and suggestions for constructing more useful databases.

A mobile app (IDoThis) for multiple sclerosis self-management: development and initial evaluation.

BACKGROUND: Multiple sclerosis (MS) is one of the most common neurological disorders worldwide, and self-management is considered an essential dimension in its control. This study aimed to develop an evidence-based mobile application for MS self-management and evaluate it. METHODS: This study was undertaken in three phases: content preparation, design, and evaluation. In the content preparation phase, the researchers extracted MS self-management needs based on related guidelines and guides, existing apps on the self-management of MS, and the field experts' views and confirmation. The design phase was conducted in five steps: defining app functionalities, depicting the wireframe, preparing the media, coding the app, and testing the app's performance. The app was developed using the Android Studio environment and Java programming language for the Android operating system. The performance of the developed app was tested separately in several turns, and existing defects were corrected in each turn. Finally, after using the app for three weeks, the app was evaluated for its short-term impact on MS management and user-friendliness using a researcher-constructed questionnaire from participants' (N = 20) perspectives. RESULTS: The IDoThis app is an offline app for people with MS that includes five main modules: three modules for training or informing users about different aspects of MS, one module for monitoring the user's MS condition, and a reporting module. In the initial evaluation of the app, 75% (n = 15) of participants mentioned that using this app improved MS self-management status at intermediate and higher levels, but 25% (n = 5) of the participants mentioned that the effect of using the app on the self-management tasks was low or was very low. The majority of users rated the user-friendliness of the app as high. The users found the sections "exercises in MS" and "monitoring of MS status" beneficial to their self-management. Still, the fatigue and sleep management sections are needed to meet users' expectations. CONCLUSION: Using IDoThis app as a self-management tool for individuals with MS appears feasible, that can meet the need for a free and accessible self-management tool for individuals with MS. Future directions should consider the users' fatigue and sleep management expectations.

Oncology Informatics for Lynch Syndrome Research and Care: A Literature Review.

PURPOSE: This study aims to review and evaluate available informatics platforms for research and management purposes of Lynch syndrome (LS) to identify gaps and needs for future development. METHODS: LS informatics tools were identified through literature search in four publication databases (1 and Scopus). First, the LS and functional elements of every informatics tools for LS were introduced. Then, current existing LS informatics tools were reviewed and explained. RESULTS: A detailed review of implemented studies shows that many types of informatics platforms are available for LS management (ie, prediction model, clinical decision support system, database website, and other tools for research and management purposes of LS). Moreover, several dimensions of existing LS informatics tools were discussed and features and positive findings were reported. CONCLUSION: Reviewing the literature reveals that several LS informatics tools were focused on gene-specific estimate, cancer risk prediction, identifying/screening patients, supporting personalized care of individuals with LS, and storing mismatch repair mutations information. Nevertheless, these platforms do not fully cover the care and research purposes. For instance, future developments of LS tools require more attention to dynamic knowledgebase, extra-colonic lynch-related cancers on the basis of precision medicine, variants of unknown significance, and support from diagnosis to surveillance for patient follow-up. Insights and recommendations provided in this study could help researchers and developers to meet the existing challenges in future developments.

Exploring Self-management Needs of Persons With Multiple Sclerosis: A Qualitative Study for Mobile Application Development.

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 self-management 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.

A review of machine learning approaches for drug synergy prediction in cancer.

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.

Expectations of Health Researchers From Academic Social Network Sites: Qualitative Study.

BACKGROUND: Today, academic social network sites' role in improving the quality of education and how investigators conduct their research has become more critical. OBJECTIVE: This study aimed to investigate Iranian health researchers' requirements for academic social network sites from a low-income country perspective. METHODS: This qualitative study with a phenomenological approach was done in 2020. In this study, 23 researchers in the health system were selected by purposive sampling. Semistructured interviews were used to collect data. Data were analyzed by MaxQDA-10 software and the content analysis method. RESULTS: We identified 2 categories of functional and technical characteristics in the study participants' expectations. Functional characteristics included facilitating communication and team activities, managing scientific publications, enhancing the process of conducting research, being informative, and sharing and trading laboratory materials and equipment. Technical characteristics of an academic social network include user management capabilities, high security and privacy, being user-friendly, and other technical features. CONCLUSIONS: Health researchers emphasized 2 functional and technical characteristics required to meet academic social network sites' expectations.

AptaNet as a deep learning approach for aptamer-protein interaction prediction.

Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .

How the US Sanctions Are Affecting the Health Research System in Iran?

BACKGROUND: In November 2018, the United States withdrew from the Joint Comprehensive Plan of Action (JCPOA), known commonly as the Iran nuclear deal, and imposed severe sanctions on Iran. This study explores the impact of US sanctions in Iran's health research system. METHODS: This phenomenological study interviewed 24 Iranian health science scholars through purposeful sampling to learn about their experiences and thoughts regarding the impact of US sanctions on Iran's health research system. RESULTS: The impact of sanctions on Iran's health research system were classified into five categories: (a) financial issues, (b) difficulty in supplying laboratory materials and (c) equipment, (d) disruption in international research collaboration and activities, and (e) other issues (e.g., increased stress and workload). CONCLUSION: This study indicated that since research centers in Iran are highly dependent on governmental budgets, sanctions have greatly affected the health research system in Iran. Financial and economic problems, restrictions in transferring funds, and the disruption in political and international relations have created many challenges for supplying medical laboratory materials and equipment for medical and health research centers in Iran.

Information technology in emergency management of COVID-19 outbreak.

Emergency management of the emerging infectious disease outbreak is critical for public health threats. Currently, control of the COVID-19 outbreak is an international concern and has become a crucial challenge in many countries. This article reviews significant information technologyIT) applications in emergency management of COVID-19 by considering the prevention/mitigation, preparedness, response, and recovery phases of the crisis. This review was conducted using MEDLINE PubMed), Embase, IEEE, and Google Scholar. Expert opinions were collected to show existence gaps, useful technologies for each phase of emergency management, and future direction. Results indicated that various IT-based systems such as surveillance systems, artificial intelligence, computational methods, Internet of things, remote sensing sensor, online service, and GIS geographic information system) could have different outbreak management applications, especially in response phases. Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers' safety, decreasing workload, saving time and cost, and drug discovery. We categorized these applications into four core topics, including diagnosis and prediction, treatment, protection, and management goals, which were confirmed by five experts. Without applying IT, the control and management of the crisis could be difficult on a large scale. For reducing and improving the hazard effect of disaster situations, the role of IT is inevitable. In addition to the response phase, communities should be considered to use IT capabilities in prevention, preparedness, and recovery phases. It is expected that IT will have an influential role in the recovery phase of COVID-19. Providing IT infrastructure and financial support by the governments should be more considered in facilitating IT capabilities.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.

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.

Computational predictive approaches for interaction and structure of aptamers.

Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.

A computational model for GPCR-ligand interaction prediction.

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.

Computational prediction of implantation outcome after embryo transfer.

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.

A Review of Machine Learning Approaches in Assisted Reproductive Technologies.

INTRODUCTION: Assisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART's makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle. AIM: This review provides an overview on machine learning-based prediction models in ART. METHODS: This article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar. RESULTS: We identified 20 papers reporting on machine learning-based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies. CONCLUSION: Machine learning-based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.

Mobile Applications for Multiple Sclerosis: a Focus on Self-Management.

INTRODUCTION: The explosion of mobile phone users along with the importance of user's role in managing their health provides a unique opportunity for m-Health applications in the management of chronic illnesses such as Multiple sclerosis (MS). AIM: To identify available MS applications and to characterize the content of MS self-management applications. METHODS: Two popular online application stores (iTunes, Google play) were searched for multiple sclerosis -related apps using the following keywords: multiple sclerosis, disseminated multiple sclerosis, disseminated sclerosis, and MS. Apps were considered eligible if they had been customized only on multiple sclerosis. First, data was extracted from the description page for any eligible application. To achieve the study goal, the secondary analysis was performed only for self-management applications. RESULTS: Search of two popular markets identified 1042 applications (747 applications from Google play, and 295 applications from iTunes). Of these, 104 unique applications met the inclusion criteria. Almost a quarter of eligible applications (26%) had been designed for multiple sclerosis self-management. Other purposes of the identified applications were diagnosing & treating (7.7%), doing tests (7.7%), connecting & communication for MS patients (4.8%), raising awareness of multiple sclerosis (15.4%), accessing to journals & news (6.7%), conferences & meetings (17.3%), supporting & donating to MS community (14.4%). CONCLUSION: It appears the mobile applications provide a multidimensional tool for patient with Multiple Sclerosis to improve their condition self-management. These applications can contribute to empowerment of the patients, and help their adherence to the therapeutic and management regimen of their conditions. Moreover, they can be utilized to collect information on the MS progress pattern in personal level for each individual patient. This information may provide health care professionals with evidence to help their patients toward enhancing self-management of their disease.