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Explore applications of conformal prediction in medicine, focusing on predictive inference for individual treatment effects and self-consistent conformal prediction techniques.
Explore insights from the largest genome-wide association study on human height, examining genetic variants, heritability, and the impact of rare variations on this complex trait.
Explore applying polygenic scores at biobank scale, examining reporting standards, reproducibility, health disparities, and clinical applications in genomic risk prediction.
Explore population genetics' role in genomic health, focusing on electronic health records, personalized medicine, and genetic disease identification in specific populations.
Explore computational methods for quantifying lethal cancer evolution. Gain insights into driver mutations, evolutionary history, and genomic correlates of prostate cancer's epigenetic landscape.
Explore interpretability and out-of-distribution generalization in deep predictive models, focusing on nuisance-induced spurious correlations and real-time Shapley value estimation techniques.
Explore gene regulatory network inference techniques using bulk and single-cell omic datasets. Learn about integrative approaches, predictive models, and applications in cellular reprogramming and single-cell genomics.
Explore rare variant analysis techniques for whole genome sequencing studies. Learn statistical methods, study designs, and powerful tools for uncovering genetic associations in large-scale genomic data.
Explore inference methods for high-throughput CRISPR screens, focusing on regulatory gene discovery in human T cells and the architecture of immune networks.
Explore how genetic research can address health inequities, focusing on case studies involving racial disparities in medical diagnoses and treatment decisions.
Explore geospatial analysis of severe mental illness, uncovering distinct hotspots and disease trajectories through computational genomics techniques.
Explore statistical rigor in genomics data science, focusing on differential expression methods, p-value calibration, and high-throughput data analysis techniques for improved accuracy in genomic research.
Learn to conduct in silico clinical trials using electronic health records, exploring innovative approaches to evaluate oncology trial eligibility criteria with real-world data and AI.
Explore integrating multiple omic data to enhance mechanistic understanding and prediction in genomics. Learn techniques for improving data interpretation and predictive modeling in computational biology.
Explore EHR data quality concerns and strategies for improvement. Learn to assess, validate, and enhance electronic health record data for more reliable research and clinical applications.
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