Brain Informatics And Health

by Mary Mehrnoosh Eshaghian-Wilner (Editor)

This volume delivers up-to-date information on the identification of newer uses, molecular mechanisms, and novel targets of existing drug candidates through the application of various experimental, biophysical, and computational approaches and techniques. The book discusses recent advances in drug repurposing strategies that are currently being used in the discovery and development of drugs against difficult-to-treat, rare, and life-threatening diseases, including microbial infections, COVID-19, parasitic diseases, cardiovascular diseases, neurological disorders, and cancer. The book also discusses the modern experimental assays (HTS) and computational techniques including informatics and databases, molecular docking and dynamics, artificial intelligence and machine learning, virtual screening and pharmacophore modeling, proteomics and metabolomics, and network pharmacology and systems biology approaches.

The book delves into innovations in AI and related computing paradigms for disease detection and diagnosis. The collected chapters elucidate the use of a variety of AI and related methodologies to address specific medical challenges. From detecting pancreatic cancer, classifying micro-emboli in stroke diagnosis, to segmenting brain tumours from MRI data, and more, the culmination of these studies underscores the transformative impact AI and digital technologies can have on healthcare, emphasising their potential to enhance medical treatment and improve patient care.

Understanding AGI: Definitions, Characteristics, and Importance in Healthcare

The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain.

This thesis covers various facets of brain image computing methods and illustrates the scientific understanding of neurodegenerative disorders based on four general aspects of multimodal neuroimaging computing: neuroimaging data pre-processing, brain feature modeling, pathological pattern analysis, and translational model development.