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.

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.

COVID-19 & an NGO and university developed interactive portal: a perspective from Iran.

On 19 February 2020, Iran reported the initial cases of novel coronavirus (2019-nCoV). As of 21 March 2020, Iran had reported 175,927 COVID-19 cases, including 8425 deaths. One of the best approaches for responding to COVID-19 is rapid detection, early isolation, and quick treatment of the disease. Studies have stated that information technology (IT) is a powerful tool for detecting, tracking, and responding to pandemic diseases. Despite the importance of IT, a lack of efficient use of information technology capacity was observed after the emergence of the new cases of COVID-19 in Iran. A web-portal can integrate different services and technologies and can support interaction between non-governmental organizations (NGO) and universities. NGOs can provide services for public health utilizing technology and its advancements. One of the important duties of these organizations is to inform and provide integrated services to the general public. An interactive portal is one of the advanced technologies that these organizations can use for health management. Medical sciences of universities play a vital surveillance role for enhancing the performance quality of NGOs. A web-portal can be a collaboration tool between health-related NGOs and medical sciences of universities. In this study, an interactive portal was developed by NGOs and a university. NGOs under the supervision and participation of Tabriz University of Medical Sciences' Center for Social Factors Research in COVID-19 management division of this portal separated classified information into two sections, informatics and services. This portal is accessible to the general public, patients, service providers, and, importantly, policymakers and presents educational and medical research information to all users. For patients and the general public in high-risk environments, increasing information security, reducing confusion regarding finding needed information, and facilitating communication are only part of the portal's benefit. It seems that web-portal capacity is needed to control COVID-19 in the digital age. The collaboration of academic and university bodies in the context of health portals can play key roles for coverage of the COVID-19 pandemic.