Jake Crawford

The paper identifies a novel mechanism for large tandem duplications in cancer by analyzing replication fork directionality and develops a classifier to distinguish SV phenotypes based on replication features.

This study compares the impact of different optimization methods on model performance and sparsity in transcriptomic prediction using LASSO logistic regression.

wenda_gpu provides a fast domain adaptation method for genomic data using GPU acceleration, achieving comparable results to the original wenda while improving cancer mutation prediction on small datasets.

This study compares neural networks and logistic regression for transcriptomic prediction, finding both linear and non-linear signals are present but not always sufficient for neural networks to outperform linear models.

This paper introduces a computational framework to integrate single-cell morphological data with RNA-seq data to uncover genes related to neuronal plasticity.

The paper evaluates the effectiveness of various -omics data types in predicting cancer mutation states and finds that RNA sequencing is often the best, but other data types can be equally effective.

This paper introduces SOPHIE, a method using generative neural networks to distinguish common from specific transcriptional responses across datasets.

MONET provides a unified toolbox for disease module identification in molecular networks using top-performing methods from the DMI DREAM Challenge.

The paper shows that removing low-degree nodes from a gene interaction network improves prediction of expression dependencies compared to a random model.

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.