| (*)Students have hands-on experience in applying AI and machine learning techniques to real-world problems in life sciences, such as drug discovery, molecular analysis, and bioinformatics. They have practical skills in implementing machine learning algorithms, analyzing biological datasets, and applying generative models for tasks like molecule design and drug target prediction.
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(*)- Implementing Machine Learning Models for Drug Discovery (k5)
Students can develop and test machine learning models for predicting drug targets, analyzing bioassays, and assessing toxicity, working with real-life biological datasets.
- Applying Molecular Representations and Chemoinformatics (k5)
Students are able to work with molecular representations, such as SMILES and molecular fingerprints, and apply chemoinformatics tools for virtual screening and QSAR modeling.
- Designing and Training Generative Models for Molecule Creation (k5)
Students can implement and train generative models to create new molecular structures, optimizing for drug-like properties and using machine learning techniques to explore chemical space.
- Analyzing Genomic and Transcriptomic Data Using Deep Learning (k5)
Students are capable of applying deep neural networks to analyze complex genomic and transcriptomic datasets, extracting meaningful patterns for biological insights and predictions.
- Working with Machine Learning for Biological Images (k5)
Students can use machine learning models to process and analyze biological images, such as microscopy data, for tasks like phenotype prediction and cellular analysis.
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(*)Students have practical knowledge of implementing machine learning models for life sciences, including drug discovery, QSAR modeling, and molecular analysis. They have gained hands-on experience with deep learning techniques for genomic data and biological image processing, applying AI methods to solve real-world problems in bioinformatics and pharmacology.
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