• Digital-Health-Conference-2020

Invited SpeakersProfile Details

Manal Kalkatawi
Manal Kalkatawi Manal Kalkatawi completed her M.S. in 2011 and her PhD in 2017 from King Abdullah University of Science and Technology (KAUST), in Computer Science focusing on Bioinformatics, Data mining, and Machine/Deep learning.


am an assistant professor at King Abdulaziz University (KAU), Faculty of Computing and Information Technology, Information Technology department. I completed my M.S. in 2011 and my PhD in 2017 from King Abdullah University of Science and Technology (KAUST), in Computer Science focusing on Bioinformatics, Data mining, and Machine/Deep learning. 

I have extensive experience in genome analysis, genomic signals recognition, and genomic sequences data extraction and processing. I have been heavily involved in designing methods and supporting systems using machine/deep learning algorithms to be applied to genomic signals recognition, genome annotation, and genome assembly. I worked on human (Homo sapiens), mouse (Mus musculus), cow (Bos taurus), fruit fly (Drosophila melanogaster) and mousear cress (Arabidopsis thaliana) genomes. Moreover, I have developed and been involved in some bioinformatics tools: DeepGSR, Dragon PolyA Spotter, INDIGO, BEACON, and Omni-PolyA.

Also, I have worked on applying deep learning in Arabic sentiment analysis and recognition of breast cancer images. Details can be found at: https://scholar.google.com/citations?user=YJgNij8AAAAJ&hl=en

All sessions by Manal Kalkatawi

  • Day 3Wednesday, January 22nd
Session 6 : Spotlight on Young Talent (Chair Prof. Robert Hoehndorf)
2:00 pm

Contributions to Digital Health in Low and High Level

Digital health (DH) is an important topic these days since it provides a lot of help to the smart world that we are approaching. This study contributes to digital health on different levels by introducing methods based on machine learning (ML).

The first contribution concerns developing generic models for the recognition of different genomic signals and regions (GSR) within eukaryotic DNA, which is a low level in DH. We developed DeepGSR, a systematic framework that facilitates generating ML models to predict GSR with high accuracy. To the best of our knowledge, no available generic and automated method exists for such task that could facilitate the studies of newly sequenced organisms and detect some diseases that are caused by specific genes. The prediction module of DeepGSR uses deep learning algorithms to derive highly abstract features that depend mainly on proper data representation and hyperparameters calibration. DeepGSR, which was evaluated on recognition of PAS and translation initiation sites (TIS) in different organisms, yields a more straightforward and more precise representation of the problem under study, compared to some other hand-tailored models, while producing high accuracy prediction results.

Secondly, we focus on deriving a model capable of facilitating the early detection of breast cancer, which is a high level in DH. We provide E-Detect, a full system with frontend (website) and backend (deep learning model), that allows the users to upload the mammograms and collect the results of the probability of having breast cancer. E-Detect uses both image processing and deep learning models and produces an acceptable prediction accuracy.

Overall, our research contributed to different aspects of DH.

Building 19, Hall 1 14:00 - 14:20 Details