OIC-COMSTECH and Ningbo University Certificate Course On Applied Biomedical AI

“Unlocking the Future of Healthcare & Life Sciences with Artificial Intelligence”

Guest Speaker
Prof. Dr. Yuzong Chen
Deputy Director, State Key Lab of Chemical Oncogenomic
Ningbo University, China

Technical Facilitator/ Speaker
Prof. Dr. Wang Likun
Ningbo University, China

Organized by:

Science, Technology and Innovation Center (STIC), COMSTECH

Workshop Details:

This certified course is designed for senior researchers, clinicians, data scientists, and academic faculty from the fields of Biotechnology, Bioinformatics, Computer Science, Pharmacy, and Life Sciences who are eager to integrate AI into biomedical applications. The program aims to equip participants with practical skills and hands-on experience in applying Artificial Intelligence to solve real-world challenges in healthcare and biomedical research especially in resource-constrained settings. Attendees will also gain insights into establishing and sustaining AI-driven biomedical research initiatives within their institutions.

Learning Objectives:

By the end of this course, the participants will:

Understand the foundational principles and practical applications of AI in biomedicine and healthcare.
Apply AI tools and techniques in data analysis, diagnostics, and research within resource-constrained environments.
Identify their own training needs to enhance AI integration in biomedical and clinical research.
Outline key requirements for establishing AI-powered biomedical research or clinical research centers in their institutions.

Target Audience:

Senior Healthcare Professionals and Researchers
Faculty Members and Academic Leaders in Life Sciences, Computer Science, and Biomedical Fields
Healthcare Administrators and Policy Makers
Government Officials involved in S&T, Health, and Education sectors
Data Scientists and AI Enthusiasts working in biomedical research

Course Module:

Lecture No.	Title	Topics Covered
1	AI Overview	What is AI? How does AI work? Machine learning vs. deep learning, history of AI
2	AI and Big Data in Biomedicine	AI and big data in language, image, audio, biomedicine; biomedical databases
3	Supervised Machine Learning Methods – Part 1	Simple supervised ML methods, k-nearest neighbor, Bayesian classifiers
4	Supervised Machine Learning Methods – Part 2	SVM, decision trees, random forest, performance measurement, training with small datasets
5	Unsupervised Machine Learning Methods	Distance/similarity measures, hierarchical clustering, K-means, fuzzy C-means
6	Machine Learning Regression Methods	Linear/polynomial regression, PCA, PLS, logistic regression, ridge, LASSO
7	Biomedical Machine Learning	Applications of ML in biomedical fields
8	Data Dimensionality Reduction, Feature Selection & Biomedical Applications	PCA, t-SNE, UMAP, visualization, feature selection methods, biomedical applications
9	Deep Learning CNN Models	Basics of Convolutional Neural Networks (CNNs)
10	Deep Learning Biomedical CNN Models	CNNs applied to biomedical data and diagnostics
11	Word Embedding and Bio molecular Encoding	Word embedding techniques, biomolecule encoding (DNA, RNA, protein), chemical and food encoding
12	Recurrent Neural Network (RNN) and Applications	RNN basics, LSTM, RNN vs. LSTM, applications in sequence data
13	Transformer	Attention mechanisms, introduction to Transformer architecture
14	Pharmaceutical Deep Learning Models	Drug discovery, deep learning in pharmaceutical applications
15	Spectroscopic Deep Learning in Biochemistry & Biomedicine	Spectroscopy techniques, DL models for spectroscopic data
16	Metabolomic ML and DL	Metabolomics, ML and DL applications in metabolomics
17	Microbial Genomics & Metagenomic Deep Learning	Microbial genomics, metagenomics, deep learning applications
18	Herbal AI	Traditional medicine, herbal databases
19	Food Machine Learning and Deep Learning	Food tech, databases, ML and DL in food science

Poster of Certified Course: Attached

Date: 23^rd – 31^st July-2025, 11-20^th August-2025

Timing: 1:30PM (Pakistan Standard Time)

Procedure to join: In-Person- Online

Registration link: Click Here

Address: COMSTECH Secretariat, 33-Constitution Avenue, G-5/2, Islamabad
Phone No. : 92 51 9220681-3
Email: comstech@comstech.org