AAAI19 @ Honolulu, Hawaii

Presenting Poster on DL in Cancer Researches

With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensionality and complexity of the data. Here we present models of deep learning (DL) and apply them to gene expression data for the diagnosis and categorization of cancer. In this study, we have developed two DL models using messenger ribonucleic acid (mRNA) datasets available from the Genomic Data Commons repository. Our models achieved 98% accuracy in cancer detection, with false negative and false positive rates below 1.7%. In our results, we demonstrated that 18 out of 32 cancer-typing classifications achieved more than 90% accuracy. Due to the limitation of a small sample size (less than 50 observations), certain cancers could not achieve a higher accuracy in typing classification, but still achieved high accuracy for the cancer detection task. To validate our models, we compared them with traditional statistical models. The main advantage of our models over traditional cancer detection is the ability to use data from various cancer types to automatically form features to enhance the detection and diagnosis of a specific cancer type.

Honolulu is amazing!

Never thought I would say this, you need to visit Hawaii y'all. :-)

For this trip, which is a sub-trip of my AAAI conference, I visit Honolulu, which is the best city of United States. The ocean was amazing, and the water is so clear. Now I know why people call this place paradise!

Deep Learnign for Computer Vision Workshop

I will present this workshop at UK ACM on March 2, come and join us!

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this workshop, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to: Implement common deep learning workflows, such as image classification and object detection. Experiment with data, training parameters, network structure, and other strategies to increase performance and capability. Deploy your neural networks to start solving real-world problems. Upon completion, you’ll be able to start solving problems on your own with deep learning.

Certification Available for Completion!!