3

There is a CSV table in this paper with 33 sets of genes.


2

Good question and the Janggu link is cool. Thanks! Kaggle is/was heavily image recognition. There isn't an MNIST type data set in genomics, in case anyone is wondering this is a classic natural language processing (NLP) type analysis to identify handwritten single digit numbers. There has been several FDA precision challenges, one being described here: FDA ...


1

There is this nice function from Kaggle: def seed_everything(seed=1234): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True seed_everything() There are several version of this function for torch, tensorflow and ...


1

I don't why this RNN k-mer Pytorch model is not producing an output. I do know that, "rnn_dropout": 0.0, ... is bad news for any ANN (artificial neural network) let alone LSTM RNN, which is a pretty complex model. Firstly, you have to assign a meaningful drop out rate and 0.5 is okay. If dropout rate = 0 as an output then the model hasn't worked. ...


1

Clearly the additional references do put Metapath2vec into good use. Its a many to many ML relationship. Most catagorization/classification particularly in deep learning is many to one. So thats the good thing about the technique you are using, you've 25 catagories, which is quite large for machine learning(ML) and large for deep learning. The technique ...


1

Actually the paper has made it clear how they did it. You just have to read the supplementary materials closer. In their figure from the paper: A is Red, C is Green, G is blue, and T is Yellow (G+R), but this is still unclear how they the 3xNxN image. In RGB, each dimension is an NxN image. Since you have three dimensions, so it's 3xNxN. The red dimension ...


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