p-koo

Pinned Repositories

creme-nn
https://www.biorxiv.org/content/10.1101/2023.07.03.547592v2
Language:Python21 1 22
deepomics
deep learning toolkit (written on top of tensorflow) to streamline computational biology analysis
Language:Python23 3 07
evoaug
Evolution-inspired data augmentations for PyTorch-based models for regulatory genomics
Language:Python20 2 03
exponential_activations
"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)
Language:Jupyter Notebook3 2 06
GOPHER
Toolset for training quantitative sequence to function models.
Language:Jupyter Notebook2 0 00
learning_sequence_motifs
"Representation Learning of Genomic Sequence Motifs with Convolutional Neural Networks" by Peter K. Koo and Sean R. Eddy
Language:Python31 4 313
residualbind
"Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks" by Koo et al.
Language:Jupyter Notebook11 2 23
squid-nn
surrogate quantitative interpretability for deepnets
Language:Python1 0 00
tensorflow_tutorial_genomics
A collection of TensorFlow tutorials to analyze genomics datasets
Language:Jupyter Notebook8 3 07
tfomics
Language:Python3 3 26

p-koo's Repositories

p-koo/learning_sequence_motifs
"Representation Learning of Genomic Sequence Motifs with Convolutional Neural Networks" by Peter K. Koo and Sean R. Eddy
Language:Python31 4 313
p-koo/tensorflow_tutorial_genomics
A collection of TensorFlow tutorials to analyze genomics datasets
Language:Jupyter Notebook8 3 07
p-koo/exponential_activations
"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)
Language:Jupyter Notebook3 2 06
p-koo/wrangler_old
data wrangling for genomics data
Language:Python2 2 00
p-koo/PRML
PRML algorithms implemented in Python
11
p-koo/theanomics
deep learning toolkit (written on top of Lasagne/Theano) to streamline computational biology analysis
Language:Python1
p-koo/vbem-SPT
variational Bayes expectation maximization of multivariate gaussians for single particle tracking diffusion analysis
Language:MATLAB1 2 02
p-koo/awesome-deepbio
A curated list of awesome deep learning applications in the field of computational biology
1
p-koo/cnn_exponential_activations
"Improving Convolutional Network Interpretability with Exponential Activations" by Peter K. Koo and Matt Ploenzke
Language:Python
p-koo/deep_learning_notes
a collection of my notes on deep learning
Language:Jupyter Notebook3
p-koo/m6a_analysis
Language:Python
p-koo/mleBIC
maximum likelihood diffusion analysis for single particle trajectories undergoing (non-)normal diffusion with Bayesian model selection (http://arxiv.org/abs/1608.01419)
Language:Matlab1
p-koo/odie-tools
command line tools to process next generation sequencing data on Harvard's Odyssey cluster
Language:Shell
p-koo/pEM
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)
Language:Matlab1
p-koo/playground
fun projects to improve lifestyle
Language:Matlab1
p-koo/proteinnet
Standardized data set for machine learning of protein structure
Language:Python
p-koo/tensorflow
Computation using data flow graphs for scalable machine learning
Language:C++1
p-koo/uncovering_regulatory_codes
"Robust Neural Networks are More Interpretable for Genomics" by Peter K. Koo, Sharon Qian, Gal Kaplun, Verena Volf, and Dimitris Kalimeris
Language:Jupyter Notebook2