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Explore hardware-algorithm co-design and processing-in-memory technologies for accelerating bioinformatics workloads, focusing on genomic sequence analysis and mapping techniques.
Explore statistically consistent methods for estimating level-1 phylogenetic networks from SNP data, focusing on rooted and unrooted structures in computational genomics.
Explore SPLASH, a statistical algorithm for efficient genomic discovery. Learn about its applications in microbial sequencing, transcriptomics, and scientific research.
Explore SARS-CoV-2 evolution through saltations and punctuated equilibrium, analyzing community structure of coordinated substitution networks for early variant detection.
Explore computational methods for modeling and analyzing epigenomic data, focusing on chromatin state annotation, gene-based modeling, and cross-species conservation in functional genomics.
Explore gene regulatory mechanisms at single-cell resolution. Discover cutting-edge techniques for analyzing multimodal data and constructing regulatory networks in genomics research.
Explore Bref4 compression techniques for efficient analysis of compressed sequence data. Learn to enhance genomic research capabilities and optimize data storage.
Explore hierarchical models for deep mutational scans, focusing on robust analysis frameworks, drug transporter biophysics, and variant effect prediction in genomics research.
Explore Pyro probabilistic programming for statistical genomics. Learn to model RNA splicing and implement isoform-specific knockdown using advanced computational techniques.
Explore time-dependent modeling using sicegar for analyzing RpoS-dependent gene transcription timing in E. coli, with applications to multiple stress responses.
Dive into efficient analysis of biobank-scale data using Genotype Representation Graphs. Explore innovative methods for estimating coalescence times and inferring whole-genome histories in large population datasets.
Explore innovative approaches to modeling Alzheimer's disease risk using real-world data, focusing on computational genomics techniques and their applications in understanding disease progression and risk factors.
Explore geometric deep learning and optimal transport techniques for uncovering cellular dynamics and metabolism in this insightful computational genomics presentation.
Explore PBWT-based algorithms for large cohort genetics, covering efficient haplotype matching, IBD detection, and innovative approaches like d-PBWT and Syllable-PBWT for genomic data analysis.
Explore classical algorithms applied to modern challenges: MCMC for tail probability estimation and EM for PET reconstruction. Gain insights into innovative adaptations of established methods for cutting-edge problems.
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