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Explore deep learning approaches for spatial modeling of single-cell data, focusing on Hi-C reads imputation and transcriptomic data prediction through graph-based methodologies.
Discover the fundamentals of mRNA design through computational approaches, exploring optimization algorithms, stability analysis, and practical applications in modern biotechnology and vaccine development.
Discover advanced techniques for phylogenomic tree estimation, including ASTRAL, DISCO, and QR-STAR methodologies for species tree inference and branch length estimation in computational genomics.
Explore genetic intra-tumor heterogeneity and evolutionary patterns across thousands of cancer genomes, examining key constraints in tumor development and progression.
Discover innovative approaches to extend biobank research benefits beyond direct participants, exploring methodologies for broader population health insights and applications.
Discover how to leverage Electronic Health Records (EHR) for psychiatric phenotyping, exploring innovative computational methods and applications in mental health research.
Discover a parameter-free framework for fast sequence alignment through UniAligner, exploring innovative approaches to computational genomics and bioinformatics analysis.
Discover advanced techniques for haplotype-resolved de novo genome assembly, focusing on phased assembly graphs and innovative methods for diploid and polyploid genome reconstruction.
Explore stochastic dynamics of microbiomes through advanced computational methods. Gain insights into microbial growth, spatial structure, and population trajectories using cutting-edge genomic analysis techniques.
Explore horizontal gene transfer phylogenetics using a random walk approach. Discover innovative methods for analyzing evolutionary relationships in the presence of lateral genetic transfer.
Explore onlineFDR control techniques for RNA-Seq data analysis, focusing on adaptive algorithms and their applications in multiple hypothesis testing scenarios.
Explore genetic genealogies in population genomics, examining their role in understanding evolutionary history, selective sweeps, and ancestral recombination. Gain insights into cutting-edge research and methodologies.
Explore discourse-level natural language understanding for scientific literature, focusing on evidence extraction, method classification, fact verification, and relation extraction techniques.
Explore variational inference techniques for large-scale genomic data analysis, integrating molecular and phenome information to uncover disease mechanisms and enhance genetic association studies.
Explore phenotype integration techniques to enhance power and specificity in biobank-based genetic studies of Major Depressive Disorder, improving accuracy in genome-wide association signals.
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