올인원 패키지 : 의료 데이터를 활용한 바이오 진단 & 신약 개발 모델 구현

01. Introduction to Biomedical Machine learning

ㆍ BBiomedical Machinlearing_01
ㆍ BBiomedicalBiomedical Machinlearing_02

02. Machine learning Workflow for biomedical data

ㆍ Machine learning Workflow for biomedical data_01
ㆍ Machine learning Workflow for biomedical data_02
ㆍ Machine learning Workflow for biomedical data_03

03. Biomedical data modality

ㆍ Biomedical data modality_01
ㆍ Biomedical data modality_02
ㆍ Machine learning methods for biomedical data

04. Basic of Deep learning

ㆍ Basic of Deep learning_01
ㆍ Basic of Deep learning_02
ㆍ Basic of Deep learning_03
ㆍ Basic of Deep learning_04

05. Colab Environment

ㆍ Colab Environment_01
ㆍ Colab Environment_02

06. Advance Biomedical Machine learning

07. (실습) Deep learning code Pattern

ㆍ Deep learning Regression code Pattern (Drug Toxicity Prediction)
ㆍ Deep learning Classification code Pattern (Drug ADME Prediction)

01. Introduction to Biomedical Image deep learning

ㆍ Introduction to Biomedical Image Deep learning

02. Biomedical Image deep learning CNN

ㆍ Biomedical Image deep learning CNN_01
ㆍ Biomedical Image deep learning CNN_02
ㆍ Biomedical Image deep learning CNN_03
ㆍ Biomedical Image deep learning CNN_04

03. Biomedical Image deep learning CNN architeture

ㆍ Convolution nerual net model architeture의 이해_01

04. Biomedical Image deep learning Object Detection and Segmentation

ㆍ Biomedical Image deep learning Object Detection and Segmentation_01
ㆍ Biomedical Image deep learning Object Detection and Segmentation_02
ㆍ Biomedical Image deep learning Object Detection and Segmentation_03
ㆍ Biomedical Image deep learning Convolutional neural network explainability

05. (이론) Biomedical Image deep learning Convolutional neural network explainability

ㆍ Biomedical Image deep learning Convolutional neural network explainability_01
ㆍ Biomedical Image deep learning Convolutional neural network explainability_02

06. (실습) Covid CT image classification

ㆍ Covid CT image classification_01
ㆍ Covid CT image classification_02

07. (실습) Histology Image weakly supervised Learning Segmentation

ㆍ Histology Image weakly supervised Learning Segmentation_01
ㆍ Histology Image weakly supervised Learning Segmentation_02

01. Introduction to Biomedical sequence deep learning

ㆍ Biomedical Sequence Modality 이해_01
ㆍ Biomedical Sequence Modality 이해_02

02. Recurrent Nerual Network

ㆍ RNN Model과 Gated RNN Model의 이해_01
ㆍRNN Model과 Gated RNN Model의 이해_02
ㆍRNN Model과 Gated RNN Model의 이해_03

03. Attention for biomedical Domain

ㆍ Attention mechanism의 이해_01
ㆍ Attention mechanism의 이해_02
ㆍ Attention mechanism의 이해_03

04. Transformer Model

ㆍ Transformer Model의 이해_01
ㆍ Transformer Model의 이해_02
ㆍ Transformer Model의 이해_03

05. (실습) Drug target interaction(DTI) prediction

ㆍ Drug target interaction(DTI) prediction_01
ㆍDrug target interaction(DTI) prediction_02
ㆍDrug target interaction(DTI) prediction_03

06. (실습) NeoAntigen prediction

ㆍ (실습) NeoAntigen prediction_01
ㆍ (실습) NeoAntigen prediction_02

07. (실습) CRISPR Repair Outcome Prediction

ㆍ CRISPR Repair Outcome Prediction_01

01. Introduction to Biomedical graph deep learning

ㆍ Biomedical Graph Modality 이해_01
ㆍ Biomedical Graph Modality 이해_02

02. Graph Neural Net for biomedical Domain

ㆍ Graph Neural Net의 이해_01
ㆍ Graph Neural Net의 이해_02

03. (실습) Drug Toxicity Prediction Using Graph Deep learning

ㆍ Graph Neural Net Model architecture의 이해_01
ㆍ Graph Neural Net Model architecture의 이해_02

04. (실습) Drug target interaction(DTI) prediction using Sequence model using Graph Neural Net

ㆍ Graph Deep learning Learning Pattern_01
ㆍ Graph Deep learning Learning Pattern_02

05. (실습) Graph Deep learning for Protein network

ㆍ(실습) Graph Deep learning for Protein network_01
ㆍ (실습) Graph Deep learning for Protein network_02