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Name: PK
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Name: PK
Type: User
A curated list of awesome deep learning applications in the field of computational biology
"Improving Convolutional Network Interpretability with Exponential Activations" by Peter K. Koo and Matt Ploenzke
https://www.biorxiv.org/content/10.1101/2023.07.03.547592v1
a collection of my notes on deep learning
deep learning toolkit (written on top of tensorflow) to streamline computational biology analysis
High Schoolers Are All You Need Team
Evolution-inspired data augmentations for PyTorch-based models for regulatory genomics
Evolution-inspired data augmentations for TensorFlow-based models for regulatory genomics
"Improving representations of genomic sequence motifs in convolutional networks with exponential activations" by Koo and Ploenzke (https://doi.org/10.1101/2020.06.14.150706)
Toolset for training quantitative sequence to function models.
"Representation Learning of Genomic Sequence Motifs with Convolutional Neural Networks" by Peter K. Koo and Sean R. Eddy
maximum likelihood diffusion analysis for single particle trajectories undergoing (non-)normal diffusion with Bayesian model selection (http://arxiv.org/abs/1608.01419)
command line tools to process next generation sequencing data on Harvard's Odyssey cluster
systems-level diffusion analysis for single particle tracks undergoing normal diffusion with no transitions between diffusion states (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004297)
sytems-level diffusion analysis for single particle tracks undergoing normal/non-normal diffusion that can transition between diffusion states (https://journals.aps.org/pre/abstract/10.1103/PhysRevE.94.052412)
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