A deep learning-based lncRNA subcellular localization predictor
You can train the model with a very simple way by the command blow: python train.py --k 3 --d 64 --s 64 --f 128 --metrics MaF --device "cuda:0"
k is the value of the k-mers features.
d is the dimension of vector of k-mer features which are trained by gensim library.
s is the number of subsequences.
f is the filter number in the CNN layer.
metrics is the evaluation metrics in the training process. "MaF" for macro f1, "ACC" for accuracy, "MaAUC" for macro auc, "MiAUC" for micro auc.
device is the device you used to build and train the model. It can be "cpu" for cpu or "cuda" for gpu, and "cuda:0" for gpu 0.
Also you can use the package provided by us to train your model.
First, you need to import the package.
from model.utils import *
from model.DL_ClassifierModel import *Then you need to create the data object and get the embedding features.
dataClass = DataClass('data.txt', validSize=0.2, testSize=0.0, kmers=3)
dataClass.vectorize("char2vec", feaSize=64)Finally, you can create the model object and start training.
s,f,k,d = 64,128,3,64
model = TextClassifier_SPPCNN(classNum=5, embedding=dataClass.vector['embedding'], SPPSize=s, feaSize=d, filterNum=f, contextSizeList=[1,3,5], embDropout=0.3, fcDropout=0.5, useFocalLoss=True, device="cuda")
model.cv_train(dataClass, trainSize=1, batchSize=16, stopRounds=200, earlyStop=10, epoch=100, kFold=5, savePath=f"out/DeepLncLoc_s{s}_f{f}_k{k}_d{d}", report=['ACC','MaF','MiAUC','MaAUC'])==Note that the model need to be named as "..._sx_fx_kx_dx" ('x' represents the parameters' value) , therefore we can get the model parameters from the name to better initialize the model architecture while in prediction.==
First, import the package.
from model.lncRNA_lib import *Then instantiate an object.
model = lncRNALocalizer(weightPath="out/xxx_sx_fx_kx_dx.pkl", classNum=5, contextSizeList=[1,3,5], map_location={"cuda:0":"cpu"}, device="cpu")Finally, do the prediction.
res = model.predict("ATCG...")
print(res)The test_set.text in Independent_test_set folder is used in comparison with lncLocator and iLoc-lncRNA. The final prediction results of three predictors (DeepLncLoc, lncLocator, iLoc-lncRNA) can be seen the supplementary materials of the paper.
The other details can see the paper and the codes.
Min Zeng, Yifan Wu, Fuhao Zhang, Chengqian Lu, Fang-Xiang Wu, Jianxin Wang, Min Li. DeepLncLoc: predict the subcellular localization of long non-coding RNAs by using deep learning techniques based on subsequence embedding
This project is licensed under the MIT License - see the LICENSE.txt file for details