서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

일시:　2018년　10월　19일 금요일 오전 11시

연사:　한영만 박사님 (KISTI)

장소:　서울대학교　25동　411호

TITLE

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction

ABSTRACT

Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this talk, it will be presented that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions.