Fusion of Signaling Pathway and Omics in Precision Cardio-Brain Oncology

Dr. Saurav Mallik received Ph.D. from the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India (2017). His Ph.D. works were conducted with the Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. He is currently a research scientist with the University of Arizona, USA. Previously, he was a postdoctoral fellow in Harvard T. H. Chan School of Public Health, University of Texas Health Science Center at Houston, and University of Miami Miller School of Medicine, USA. He has co-authored more than 210 research articles with a Google H-index of 27. His research interests include computational biology, biostatistics, machine learning, and soft computing. He was the recipient of CSIR Research Associate, Government of India (2017), “Emerging Researcher in Bioinformatics” Award from Bioclues, India (2020). He is a member of IEEE, ACM, AACR, and BioClues. His research has been covered by various national and international media, TV channels, and newspapers.Dr. Kanad Ray is the director, Amity Cognitive Computing and Brain Informatics Centre, professor of physics and electronics and communication, and head of physics at Amity University Rajasthan (AUR), India. He obtained M.Sc. and Ph.D. degrees in physics, respectively, from Calcutta University and Jadavpur University, India. Prof. Ray’s research interests include cognition, communication, electromagnetic field theory, wave propagation, microwave, and applied physics. He served as the editor of various Springer Book Series. He was an associated editor of various international reputed journals.  He has established an MOU between Amity University Rajasthan and University of Montreal, Canada, for collaboration. He is an associated researcher of Université de Montréal, Canada, visiting professor/scientist to Benemérita Universidad Autónoma de Puebla, Mexico, Universiti Teknologi Malaysia (UTM), Malaysia, and Calcutta University, India. He organizes international conference series, viz., TCCE and TEHI as a general chair. Dr. Mufti Mahmud is a professor in the information and computer science department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. Dr. Mahmud holds a Ph.D. in information engineering (bioengineering curriculum) from the University of Padova, Italy. He received a first-class B.Sc. in computer science from the University of Madras (India) and an M.Sc. in computer science from the University of Mysore (India) with distinction. He then pursued an M.S. in Nano and Micro Electromechanical Systems (NEMS/MEMS) at the University of Trento, Italy. With over 20 years of experience in academia and industry in the UK, Italy, Belgium, Bangladesh, and India, Dr. Mahmud has held several leadership positions, including member of the University Shadow Executive Team at Nottingham Trent University (NTU), UK, and coordinator of the Computer Science and Informatics (B11) Unit of Assessment for the Research Excellence Framework at NTU.

• A Comprehensive Study of Machine Learning and Deep Learning Methods for Medical Brain Imaging.- A Systematic Review of Brain Tumor Classification from MRI Scans through the Last Decade.-  Comparative Analysis of Logistic Regression, Random Forest, XGBoost, LightGBM, and Voting Classifier for Early Stroke Detection.- Machine Learning and Deep Learning for Multi-Omics and Signal-Based Biomarker Discovery in Cardio–Brain Oncology: A Comprehensive Survey.- Intelligent Brain Tumor Detection System Based on YOLOv8 Deep Learning Model.- Spatial Transcriptomic Deep Learning and Gene-Based Machine Learning for SLE Diagnosis: A Comparative Analysis.- CANCER SIGNALING PATHWAYS AND CANCER BIOLOGY.- Computational Detection of Pigmented Skin Cancer Using Machine Learning Techniques.- The Efficacy of Internet of Medical Things (IoMT) and Cloud-Fog Computing in Monitoring Neuro-Oncology Patients: A Systematic Literature Review.- Alzheimer's Disease Progression Prediction with Machine Learning.- Deciphering TP53-Mediated Pathways in Cancer Through Protein Interaction Analysis.- Accurate and Early Detection of Dysarthria Using a Multimodal Deep Learning Methodology.-Advancing Lung Cancer Detection Using Optimized Fine Tuning Data-Efficient Image Transformers.