• Digital-Health-Conference-2020

Invited SpeakersProfile Details

Dr. Hammad Naveed
Dr. Hammad Naveed Dr. Hammad Naveed is an Associate Professor in the Computer Science Department at the National University of Computer & Emerging Sciences, Pakistan.

Biography

Dr. Hammad Naveed is an Associate Professor in the Computer Science Department at the National University of Computer & Emerging Sciences, Pakistan. Previously he was a Research Assistant Professor at the Toyota Technological Institute at Chicago, University of Chicago 2014-2017. Hammad Naveed received his Ph.D. in Bioinformatics from University of Illinois at Chicago in 2012. He spent the following two and half years as a Postdoctoral Fellow at the CAS-MPG Partner institute for Computational Biology, Shanghai China and the Computational Biology Research Centre at KAUST, Saudi Arabia. His research interests include algorithm development, genomics, characterization of protein-protein interactions, characterization of protein-drug interactions, pharmacovigilance, protein function prediction and disease identification using various imaging techniques. 

All sessions by Dr. Hammad Naveed

  • Day 3Wednesday, January 22nd
Session 5 : AI & Computational Resources (Chair Dr. Katsuhiko Mineta)
10:45 am

Learning to diagnose Cancer

In this talk, I will discuss the use of natural language processing methods to read scientific literature and learn the implicit features that cause certain genetic mutations to be pathogenic. Precisely, our method ingests the bio-medical literature and produces its fixed representation via exploiting state of the art NLP methods like word2vec and tf-idf.

These representations are then fed to machine learning predictors to identify the pathogenic versus neutral variations. I will also discuss the use of deep learning methods to classify breast cancer into four classes. I will discuss the use of scaled networks to diagnose the various stages of breast cancer. Both of our methodologies significantly outperform previous state-of-the-art studies on publicly available datasets.

Building 19, Hall 1 10:45 - 11:10 Details