Application of Artificial Intelligence in Pharmaceutical and Biomedical Studies.

Background: Artificial intelligence (AI) is the way to model human intelligence to accomplish certain tasks without much intervention of human beings. 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 have been a significant amount of research works using AI in order to determine the advantages and disadvantages of its applicabication and, future perspectives that impact different areas of society.

Detection of Disease-Specific Parent Cells Via Distinct Population of Nano-Vesicles by Machine Learning.

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 the augmented release of nano-vesicles under pathological conditions, including from RPE as well as cancer-associated ECs, which take part in various biological processes, including intercellular communication in disease progression.

An Integrated-OFFT Model for the Prediction of Protein Secondary Structure Class.

Background: Proteins are the utmost multi-purpose macromolecules, which play a crucial function in many aspects of biological processes. For a long time, sequence arrangement of amino acid has been utilized for the prediction of protein secondary structure. Besides, in major methods for the prediction of protein secondary structure class, the impact of Gaussian noise on sequence representation of amino acids has not been considered until now; which is one of the important constraints for the functionality of a protein.