| Course Name | Artificial Intelligence applications in Medicine |
| Course Code | 24MMD304 |
| Program | B. Sc. Honors (with Research) in Molecular Medicine |
| Semester | 5 |
| Credits | 3 |
| Campus | Kochi |
Machine learning is a discipline that integrates different sub-disciplines of informatics, chemistry, statistics, molecular biology, biochemistry and molecular medicine. Machine learning methods are essential and increasingly used in various fields of biomedicine from biomarker development to drug discovery. This course aims at to develop skills in using appropriate cutting-edge quantitative methods to fully exploit complex and high dimensional data in molecular medicine.
Introduction to Machine learning; Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) ; Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) ; Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI. Basics of BioPython and its use in Structural bioinformatics discipline.
Molecular docking with deep learning, convolution neural network and its potential in virtual screening and binding affinity prediction, machine learning algorithms as scoring functions and rank binding poses. Application of deep learning to predicting protein structure- A deep convolution neural network for bioactivity prediction in structure based drug discovery;
Supervised and unsupervised representation to predict the future of patients from the electronic health records; deep dynamic memory model for predictive medicine, deep survival analysis; medical imaging data- deep learning for identifying metastatic breast cancer.
Machine learning techniques in drug discovery and development, Machine learning applications in cancer prognosis and prediction, Emphasis on papers illustrating techniques for data-driven machine learning analysis of big data in Digital medicine
Practical aspects in Machine learning and project exercise in computer aided molecule design.
Course Outcomes: (CO)
CO1: Basic understanding of different machine learning concepts, deep learning and its application in structural bioinformatics.
CO2: Machine learning techniques and its application in Chemoinformatics and drug discovery program. Its use in different case studies in molecular medicine field.
CO3: Introduction to clinical applications of machine learning and its use in different diagnosis of disease process.
CO4: Understanding the machine learning process in cancer and other medical cases by review of latest literature and bringing in latest knowledge in the field.
CO5: Different machine learning project applications in molecular medicine implemented and executed.
Program Outcomes (PO)
PO1: Bioscience Knowledge
PO2: Problem Analysis
PO3: Design/Development of Solutions
PO4: Conduct Investigations of complex problems
PO5: Modern tools usage
PO6: Bioscientist and Society
PO7: Environment and Sustainability
PO8: Ethics
PO9: Individual & Team work
PO10: Communication
PO11: Project management & Finance
PO12: Lifelong learning
3 = High Affinity, 2 = Medium Affinity, 1 = Low Affinity, – = No Affinity
| c
C O |
PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 |
| CO 1 | 2 | 2 | 2 | 2 | 2 | 1 | – | – | – | – | – | 2 |
| CO 2 | 3 | 2 | 2 | 2 | 2 | 1 | – | – | – | – | – | 2 |
| CO 3 | 3 | 3 | 2 | 2 | 2 | 1 | – | – | – | – | – | 2 |
| CO 4 | 3 | 3 | 2 | 2 | 2 | 1 | – | – | – | – | – | 2 |
| CO 5 | 3 | 3 | 2 | 2 | 2 | 1 | – | – | – | – | – | 2 |
Program Specific Outcomes (PSO)
PSO 1 – Chemical and physical basis of biology
PSO 2 – Computational science in biology and medicine
PSO 3 – Biochemical and physiological complexity in biology and medicine
PSO 4 – Molecular technology in biology and medicine
PSO 5 – Cell based approaches in diagnosis and therapy
PSO 6 – Microorganisms in medicine
PSO 7 – Nanoscale entities and its significance in medicine
PSO 8 – Tissue architecture engineering in medicine
PSO 9 – Compounds as drugs and its efficacy
PSO 10 – Bioinformatics and artificial intelligence in medicine
PSO 11 –Technology in personalizing medicine
PSO 12 – Protein structural complexity in medicine
PSO 13 – Projecting science and medicine to public
| –
C O |
PSO1 | PSO2 | PSO3 | PSO4 | PSO5 | PSO6 | PSO7 | PSO8 | PSO9 | PSO10 | PSO11 | PSO12 | PSO13 |
| CO 1 | 3 | 3 | 2 | 1 | – | 1 | – | – | 1 | 3 | 1 | 2 | 1 |
| CO 2 | 3 | 3 | 2 | 1 | – | 1 | – | – | 1 | 3 | 1 | 2 | 1 |
| CO 3 | 3 | 3 | 2 | 1 | – | 1 | – | – | 3 | 3 | 2 | 2 | 1 |
| CO 4 | 3 | 3 | 2 | 1 | – | 1 | – | – | 3 | 3 | 2 | 2 | 1 |
| CO 5 | 3 | 3 | 2 | 1 | – | 1 | – | – | 3 | 3 | 1 | 2 | 1 |
|
Internal Assessment – 50% |
||
|
Periodical 1 |
Exam |
20% |
|
Periodical 2 |
Exam |
20% |
|
Continuous Assessment |
Assignment/Test/Quiz |
10% |
|
50% |
||
|
End Semester Examination- 50% |
||
|
Theory Exam |
50% |
|
|
50% |
||
|
Total |
100% |
1) Introduction to Machine Learning, By Ethem Alpayd in, 4th Edition, MIT press, 2020
2) Artificial Intelligence in Drug Discovery, Editor: Nathan Brown, Copyright year 2021; Print ISBN 978-1-78801-547-9
3) Deep Learning for the Life Sciences, by Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Released April 2019, Publisher(s): O’Reilly Media, Inc., ISBN: 9781492039839
4) https://www.mdpi.com/journal/biomolecules/special_issues/ML_drug_design
5) www.biopython.org
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