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5utr_optimizer

Code for 5UTR project

anthem

autoencoders_keras

Automatic feature engineering using deep learning and Bayesian inference using Keras and TensorFlow.

bertviz

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

bine

BiNE: Bipartite Network Embedding

cblaster

Find clustered hits from a BLAST search

chemlstm

Using LSTM based language model with tokenized chemical name to predict the reaction of organic chemicals

conditional-sequence-to-sequence-vae

Conditional Sequence-to-sequence VAE

cvae-gan-zoos-pytorch-beginner

For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.

deepsynergy

dti-cdf

A help file for DTI-CDF for DTIs prediction.

dti-mlcd

Predicting drug-target interaction using multi-label learning with community detection method (DTI-MLCD)

ecco

Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).

efficient-evolution

Efficient evolution from protein language models

emotion-recognition1

a deep neural network with TensorFlow to slove an emotion task

ensc813-project

Deep learning applied to chemical reaction prediction.

esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

featureselection-fsrv

Novel Decomposing Model with Evolutionary Algorithms for Feature Selection in Long Non-Coding RNAs

human_5utr_modeling

Modeling of human 5′ UTRs enables the design of new sequences for target levels of translation and 5′ UTR variant prediction.

ilearnplus

iLearnPlus is the first machine-learning platform with both graphical- and web-based user interface that enables the construction of automated machine-learning pipelines for computational analysis and predictions using nucleic acid and protein sequences.

ires

iris_multioutput

Multi output Keras model on the iris dataset. I am adding a regression value to each class so that I can implement classification and regression in the same model. Classes are flower types: Iris-setosa, Iris-versicolor,Iris-virginica So I am adding another output for flower price

lab4-conditional_sequence_to_sequence_vae

ligdream

Novel molecules from a reference shape!

lit

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

lrf-dtis

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

mathfeature

Feature Extraction Package for Biological Sequences

mda-gcnftg

models

Models and examples built with TensorFlow

mttrans