Analytics for BioMedical/Gene Expression Application

After 15 years of research in the field of AI and Bio-Medical applications, we have finished our extensive report on the use of AI and Machine learning for Bio-Medical applications. This includes classification of varieties of Cancers using Gene Expression using microarray data some with over 37,000 attributes (Gene’s expressions). These are just simple examples of class of Big Data Analytics, with 10s of thousands of attributes. We are looking forward to share the results and findings through both public and private presentations during the next few months to come. We have focused on many key types of Cancers such as Breast Cancer, Colon Tumor, Leukemia, Lung Cancer, Ovarian Cancer, Prostate Cancer, and other Bio-Medical applications such as Central Nervous Systems, Genomic Sequences, and Diffuse Large B-Cell Lymphoma. We have used the public dataset, and we have showed that our techniques outperform of the existing state-or-the-art techniques, with accuracy of %95-%98 (test runs) and %100 for optimized models. We have used the public dataset, and we have showed that our techniques outperform of the existing state-or-the-art techniques, with accuracy of %95-%98 (test runs) and %100 for optimized models.

Breast Cancer

Central Nervous System

Colon Tumor

Diffuse Large B-Cell Lymphoma (DLBCL)

1. DLBCL-Stanford

2. DLBCL-Harvard

3. DLBCL-NIH

Leukemia

• Leukemia-ALLAML (WhiteHead, MIT)

• Leukemia-MLL (WhiteHead, MIT)

• Leukemia-subtype (Stjude)

Lung Cancer

• LungCancer-DanaFarberCancerInstitute-HarvardMedicalSchool

• LungCancer-BrighamAndWomenHospital-HarvardMedicalSchool

• LungCancer-Michigan

• LungCancer-Ontario

Ovarian Cancer

• OvarianCancer-NCI-PBSII-061902

• OvarianCancer-NCI-QStar

Prostate Cancer

Genomic Sequences

• Translation Initiation Site Prediction

• Polyadenylation Signal Prediction