Background: Artificial intelligence (AI) is the way to model the human intelligence to accomplish certain task without much intervention of human being. The term AI was first used in 1956 with The Logic Theorist program, which was designed to simulate problem solving ability of human beings. There has been a significant amount of research using AI in order to determine advantages and disadvantages of the applicability and, the future perspectives that impact on different areas of society. Even the remarkable impact of AI can be transferred to the field of healthcare with its use in pharmaceutical and biomedical studies crucial for the socioeconomic development of the population in general within different studies we can highlight those that have been conducted with the objective of facing diseases, such as cancer, neurodegenerative diseases, among others. In parallel, the long process of drug development also requires the application of AI to accelerate research in medical care. Methods: This review is based on research material obtained from PubMed up to Jan 2020. The search terms include ―artificial intelligence‖, ―machine learning‖ in context of the research in pharmaceutical and biomedical applications. Results: This study aimed to highlight the importance of AI in biomedical research also recent studies support the use of AI to generate tools using patient data to improve outcomes. Other studies have demonstrated the use of AI to create prediction models to determine response to cancer treatment. Conclusion: The application of AI in the field of pharmaceutical and biomedical studies have been extensively utilized, including cancer research, for diagnosis as well as prognosis of disease state. It has become a tool for researchers in the management of complex data obtaining complementary results to conventional statistical analyses. AI increase the precision in estimation of treatment effect in cancer patients and determine prediction outcomes.

Background: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. AMD is primarily caused due to the dysfunction of retinal pigment epithelium (RPE), whereas endothelial cells (ECs) play one of the major roles in angiogenesis; an important process which occurs in malignant progression of cancer. Several reports suggested about the augmented release of nano-vesicles under pathological conditions, including from RPE as well as cancer-associated ECs, which take part in various biological process including intercellular communication in disease progression. Importantly, these nano-vesicles are around 30-1000 nm and carry fingerprint of their initiating parent cells (IPCs). Therefore, these nano-vesicles could be utilized as the diagnostic tool for AMD and cancer, respectively. However, the analysis of nano-vesicles for biomarker study is confounded by their extensive heterogeneous nature. Methods: To confront this challenge, we utilized artificial intelligence (AI) based machine learning (ML) algorithms such as support vector machine (SVM) and decision tree model on the dataset of nano-vesicles from RPE and ECs cell lines with low dimensionality. Results: Overall, Gaussian SVM demonstrated highest prediction accuracy of the IPCs of nanovesicles, among all the chosen SVM classifiers. Additionally, the bagged tree showed highest prediction among the chosen decision tree-based classifiers. Conclusion: Therefore, overall bagged tree showed the best performance for the prediction of IPCs of nano-vesicles, suggesting the applicability of AI based prediction approach in diagnosis and prognosis of pathological conditions, including non-invasive liquid biopsy via various biofluids-derived nano-vesicles.

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An integrated-order-function-frequency-time (OFFT) model is proposed for the prediction of protein secondary structure. For the first time, we report the effect of Gaussian noise on the prediction accuracy of protein secondary structure and proposed a robust integrated-OFFT model, which is effectively noise resistant.

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