Deep Learning Imaging - Applications in Genomics and Embryology
Iman Hajirasouliha
November 2, 2022, Wednesday, 3:00 PM - 4:00 PM EST
In this talk, we discuss two deep-learning imaging models with applications in (a) human genome structural variant detection and (b) assessing embryo quality and the prediction of aneuploid embryos.

Structural variants (SV) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine and our understanding of human genetics. Existing SV callers rely on hand-engineered features and heuristics to model SVs, which cannot easily scale to the vast diversity of SV types nor fully harness all the information available in sequencing datasets. Since deep neural networks can learn complex abstractions directly from the data, they offer a promising approach for general SV discovery. Here we present an extensible deep learning framework, Cue, to call and genotype SVs.

One challenge in the field of in vitro fertilization is the selection of the most viable embryos for transfer. Manual Morphological quality assessment and morphokinetic analysis both suffer from intra- and inter-observer variability. A third method, pre-implantation genetic testing for aneuploid (PGT-A), has limitations including its invasiveness and cost. We hypothesize that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. We present a non-invasive and automated method of embryo evaluation that uses deep learning to assess embryo quality and predict embryo ploidy status.
Iman Hajirasouliha, Ph.D. is an Associate Professor of Computational Genomics in Computational Biomedicine and of Physiology and Biophysics, Physiology and Biophysics. Dr. Hajirasouliha and his lab team are passionate about developing new algorithms, machine learning and deep learning methods, and their applications to genomics, metagenomics and cancer research. Some of the current projects in the lab include characterizing human genomes and metagenomes sequenced by exciting new technologies, quantifying cancer evolution, a study of tumor heterogeneity using genomics and digital pathology images.