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Explore fine-mapping techniques in genetics, covering basic principles, statistical methods, and applications in identifying causal variants for complex traits and diseases.
Explore the design and application of covalent binding small molecule probes for enzyme monitoring, with focus on cancer and infectious disease imaging and therapy targets.
Explore genetic constraint calculation and rare variation modeling techniques for understanding human population genetics and disease mechanisms.
Explore advanced techniques for integrating and analyzing single-cell data across modalities using Seurat v5, including bridge integration and atomic sketching for improved scalability and clustering.
Explore innovative approaches to understanding cancer through AI and machine learning with Trey Ideker's insightful presentation on building a computational mind for cancer research.
Explore genetic diversity in human pluripotent stem cell models and its importance for medical research. Learn about complex trait genetics and cutting-edge DNA sequencing methods.
Explore mono- and bi-valent allele impact in genetics, focusing on population bottlenecks, disease-causing variants, and heterozygous effects. Gain insights from Finland research project.
Explore foundation models for therapeutic design and treatment prediction, focusing on novel insights in biomedical research and machine learning advancements.
Explore the evolution of genome editing and programmable medicine with CRISPR pioneer Feng Zhang and his lab members, discussing advancements and future potential in this field.
Explore cutting-edge research and developments in rare disease therapies through expert talks on genetics, drug development, biomarkers, and patient-centric approaches.
Explore generative AI applications in single-cell response modeling, covering data integration, representation learning, and multiomics analysis for biomedical insights.
Explore sparsity in epistatic interactions to enhance fitness function modeling. Learn about Walsh-Hadamard transform, Graph Fourier transforms, and Epistatic Net for improved protein function prediction.
Explore contrastive latent variable models for analyzing differential expression in sequencing data, enhancing case-control comparisons in transcriptomics with novel approaches to capture condition-specific variations.
Explore genome harmonization techniques for deeply sequenced data, covering quality control, structural variants, and PCA analysis using cloud computing.
Explore suSIEx, a cutting-edge method for analyzing genetic data in medical and population genetics. Learn its principles, applications, and potential impact on understanding complex traits and diseases.
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