Carl Kingsford

seq2ribo predicts ribosome location profiles from RNA sequences using a hybrid simulation and machine learning approach, achieving high correlation with experimental data.

AutoTuneX improves transcript assembly accuracy by predicting optimal parameters for RNA-seq samples.

CodonRL improves codon sequence optimization using reinforcement learning with structural feedback and expert demonstration, outperforming existing methods on multiple metrics.

The paper shows that sparse, random sampling of self peptides in the thymus is sufficient for T cell tolerance to most peripheral self peptides, supported by experimental data and machine learning analysis.

ARCADE enables flexible, controllable codon design using activation engineering from genomic language models to steer biological metrics like Codon Adaptation Index and GC content.

DTMol is a diffusion model for pocket-based molecular docking that outperforms existing methods on benchmark datasets and identifies new JAK2 inhibitors.

The paper introduces Xron, a semi-supervised basecaller for detecting m6A methylation from nanopore sequencing by combining synthetic and experimental data.

The paper investigates the impact of using canonical k-mers in sketching methods for genomic sequences and proposes solutions to address resulting issues.

This paper introduces a scalable optimization algorithm for solving the Beltway and Turnpike problems with uncertain measurements, showing improved accuracy and scalability on synthetic and partial digest data.

The paper introduces a novel algorithm to improve Hi-C contact matrices by inferring linear genomes from Hi-C data using genome graphs, resulting in more accurate structural representations.

This paper introduces masked minimizers, a unifying framework for sequence sketching methods, and proposes a new metric to evaluate and optimize them for better genomic analysis.

The paper evaluates how genome graph expressiveness affects the discrepancy between genome graph distance and string set distance, introducing improvements to reduce estimation errors.

The paper introduces a differentiable framework for optimizing sequence-specific minimizer schemes, showing improved performance on genomic sequences.

VariantStore efficiently indexes and queries genomic variants across thousands of samples, demonstrating scalability with large datasets.

The paper introduces polar sets for sequence-specific minimizer construction, showing their effectiveness on the human reference genome.

The paper introduces a hierarchical method to select representative RNA-seq samples from large databases, achieving significant runtime and memory reductions while maintaining performance comparable to direct methods.

The paper introduces RLZ-Graph, a space-efficient genome graph constructed using Lempel-Ziv compression, reducing disk space by 40.7% compared to colored de Bruijn graphs.

HARVESTMAN is a framework for hierarchical feature learning and selection from whole genome sequencing data, showing improved performance over binary SNP representations in breast cancer classification.

The paper confirms that considering unexpected fragment origins improves transcript quantification accuracy in RNA-seq simulations and shows that Srivastava et al.'s methodology enhances this accuracy.

This paper develops an algorithmic framework to optimize TAD definitions and biological properties, showing that TADs vary by cell type and are better represented as subpopulations rather than a single set.

The paper presents an Integer Linear Programming approach for optimal ancestral PPI network reconstruction under the DMC model, showing improved likelihood and agreement with biological evidence.

The paper introduces a probabilistic method to correct a previously uncharacterized Hi-C artifact caused by incorrect alignment in repeat regions.

The paper evaluates how alignment and mapping methods affect transcript quantification accuracy and introduces a new method called selective alignment.

The paper introduces a randomized algorithm called Miniception to design minimizers with optimal density guarantees for practical bioinformatics applications.

The paper introduces a new seeding framework for read mapping that reduces seed frequencies and improves efficiency by using context-aware seeds with confidence radii.

The paper introduces a computational method to detect transcriptomic structural variants in heterogeneous samples by solving the Multiple Compatible Arrangements Problem, showing improved performance over existing tools.

This paper introduces a statistical nonparametric method to identify consistently important features across samples and demonstrates its application to RNA sequencing data.

The paper addresses non-identifiability in RNA-seq expression quantification by calculating confidence ranges for transcript abundances, showing up to 50% of transcripts may have inaccurate estimates.

This paper evaluates how transcript assembly improves transcriptome analysis using long-read sequencing, showing Scallop-LR identifies more known transcripts and novel isoforms than Iso-Seq Analysis.

The paper introduces an anomaly detection method to identify and correct misquantifications in RNA-seq data, reducing false differential expression predictions.