Github Snuhl Crain Futusa

Github Snuhl Crain Futusa Contribute to snuhl crain futusa development by creating an account on github. We designed a deep learning program for protein function prediction, using only amino acids and named it ‘futusa’ (function teller using sequence alone). compared with other baseline method, futusa achieved a better performance in protein function prediction.

Crain Design Github After trained for monooxygenase activity, futusa successfully predicted the impact of point mutations on phenylalanine hydroxylase, which is responsible for an inherited metabolic disease pku. This program was named futusa (function teller using sequence alone). it applied sequence segmentation during the sequence feature extraction process, by a convolution neural network, to train the regional sequence patterns and their relationship. Snuhl crain has 3 repositories available. follow their code on github. Def model futusa (seg size, aa letter): inputs = input (shape= (seg size,len (aa letter))) conv1x1 = conv1d (filters=21, kernel size=1, strides=1, padding='same') (inputs) conv1x1 act = activation ('relu') (conv1x1) conv1d 2 = conv1d (filters=32, kernel size=2, strides=1, padding='same') (conv1x1 act) conv1d 2 nor = batchnormalization () (conv1d 2).

Crain99 Crain Github Snuhl crain has 3 repositories available. follow their code on github. Def model futusa (seg size, aa letter): inputs = input (shape= (seg size,len (aa letter))) conv1x1 = conv1d (filters=21, kernel size=1, strides=1, padding='same') (inputs) conv1x1 act = activation ('relu') (conv1x1) conv1d 2 = conv1d (filters=32, kernel size=2, strides=1, padding='same') (conv1x1 act) conv1d 2 nor = batchnormalization () (conv1d 2). Model trainer.py futusa readme.md snuhl crain update readme.md db2efec · 4 years ago history. Also, since futusa is not a ready to use predictor, users should modify training dataset, optimize preprocessing parameters, and train it according to their purposes. Deep learning technologies have been adopted to predict the functions of newly identified proteins in silico. however, most current models are not suitable for poorly characterized proteins because they require diverse information on target proteins. Github is where people build software. more than 150 million people use github to discover, fork, and contribute to over 420 million projects.