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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7800 条 Path
共 7800 条 Path · 第 21 / 780 页
This learning path introduces the essential concepts of materials science, covering materials classification, structure-property relationships, and characterization techniques. It is designed for high school STEM students and assumes basic chemistry knowledge.
A beginner-friendly introduction to computational materials science, covering the scope, history, materials classes, and computational methods. Designed for high school students with basic chemistry and physics knowledge.
This advanced graduate-level path equips aspiring researchers with rigorous research methods for computational biology, covering experimental design, data analysis, literature review, ethics, and reproducibility. It integrates comprehensive biology background with quantitative and computational skills to design and execute robust, reproducible research.
This learning path guides structural biology students from foundational concepts in protein structure and machine learning to advanced deep learning methods for structure prediction, including AlphaFold, evaluation metrics, and applications in protein design. It emphasizes the conceptual and practical understanding necessary to critically assess and apply these tools.
This learning path guides microbiology students through the computational analysis of microbiomes, covering composition analysis, metagenomic techniques, functional profiling, and host-microbe interactions. It bridges microbiology and bioinformatics, emphasizing practical skills and conceptual understanding.
This graduate-level learning path equips genomics researchers with computational skills to analyze long-read sequencing data. It covers long-read technologies, assembly algorithms, structural variation detection, and methylation analysis, with a foundation in programming and genomics. The path emphasizes hands-on application and critical evaluation of methods.
This advanced graduate-level learning path equips drug discovery researchers with the knowledge and skills to apply AI, particularly deep learning, generative models, and reinforcement learning, to drug design and protein engineering. It begins with essential machine learning and cheminformatics foundations, progresses through deep learning architectures for molecular representation, and culminates in advanced topics like generative molecular design and reinforcement learning for de novo drug discovery.
This learning path guides advanced biology students through the core concepts and computational methods of spatial transcriptomics. It covers experimental technologies, data structures, preprocessing, spatial mapping, and pattern discovery, with a focus on practical analysis skills.
This advanced graduate-level path equips pharmacology students with the skills to model and predict drug effects at a systems level. It covers core concepts in systems biology, pharmacodynamics, and pharmacokinetics, then integrates them into drug-target interaction modeling, pathway analysis, and response prediction. The path emphasizes practical computational methods and culminates in a capstone project that applies these techniques to a real-world drug response problem.
This path equips bioinformatics practitioners with the operational skills needed to manage, scale, and automate bioinformatics workflows. It covers pipeline management, high-performance computing, cloud computing, and robust data management, with a focus on practical application in professional settings.