![]() However, if different nets are used then netName at Line 6 of func_FeatureSelection.m file should be changed accordingly. The input is model.mat (from func_TrainModel) and related. Also, the model file model.mat detailing the nets will be stored.įunc_FeatureSelection: This will find activation maps at the ReLu layer, perform Region Accumulation (RA) step and Element Decoder step to find gene subset. ![]() For example, file 0.035778.mat in DeepResults folder tells the hyper-parameters at validation error 0.035778. mat files, where the file name depicts the best validation error achieved. The best evaluation is stored in DeepResults folder as. By default it is fifty i.e., ‘MaxObj’,50,…, change to ‘MaxObj’,2,… (for example). Note: To test the code more quickly (as discussed above), please replace the number of the maximum objective function to 1 or 2 in Line 20 of DeepInsight_train_norm_CAM.m file. It uses Train set and Validation set to tune and evaluate the model hyper-parameters. The nets are modified so that feature selection can be performed. It optimizes hyper-parameters using the Bayesian Optimization Technique. However, the user may change the net as required. The image datasets are stored as Out1.mat or Out2.mat.įunc_TrainModel: This function executes the convolution neural network (CNN) using SqueezeNet architecture. Once the pixel locations are obtained, all the non-image samples are converted to image samples. The Test and Validation sets are not used to find pixel locations. All the parameter settings can be done in this file.įunc_Prepare_Data: This function loads the data, splits the training data into the Train and Validation sets, normalizes all the 3 sets (including Test set), and converts non-image samples to image form using the Train set. However, the main file is ‘DeepFeature.m’. If the loop continues then the value of X will increment to 3, 4, … i.e., repeating DeepFeature model to find a smaller subset of genes.ĭeepFeature_pkg has 4 folders: Data, DeepResults, FIGS and Models. ~/DeepFeature_pkg/FIGS/Run1/StageX where X is the current stage i.e., ‘2’ here. Similarly, all the figures will be stored in a folder ~/DeepFeature_pkg/Models/Run1/StageX where X is the current stage i.e., ‘2’ here. A few messages will be displayed on the Command Window of Matlab, such asĪll the results will be stored in current stage folder Open the main file, DeepFeature.m and Run. This will run the code in one loop otherwise next loop with continue. Go to Line 18 of DeepFeature.m and change Parm.DesiredGenes = 8000. ![]() Go to Line 20 of DeepInsight_train_norm_CAM.m file and replace ‘MaxObj’,50,…, to ‘MaxObj’,2,… (This will allow only 2 objective functions to run on GPU). To check and run the package in a faster way, Run to check if the package is installed correctly Place the data file ‘dataset1.mat’ in the folder, ~/DeepFeature_pkg/Data/ĭownload and Install Squeezenet in Matlab, see details about Squeezenet from MathWorks link. Gunzip and untar as follows: > gunzip DeepFeature_įollow the link: RNA-Seq Data to download the RNA-seq data (caution: data size is 1.5GB). Sharma A, Lysenko A, Boroevich KA, Vans E, Tsunoda T, DeepFeature: feature selection in nonimage data using convolutional neural network, Briefings in Bioinformatics, 22(6), bbab297, 2021 Download and Installĭownload Matlab package DeepFeature_ from the link above. A real world application of DeepFeature to publicly available cancer data identified gene sets with significant overlap to several cancer-associated pathways suggesting the potential of this method to discover biomedically meaningful connections. ![]() This approach employs CNN for element or gene selection on non-image data. This approach builds an image by arranging elements (or genes) by finding similarity among them and then by mapping the non-image values on to these aligned pixel locations. tar.gz DeepFeatureĭeepFeature converts non-image samples into image-form and performs element selection via convolutional neural network (CNN). DeepFeature The package has been tested on Ubuntu 18.10 with Matlab R2020a View on GitHub Download. DeepFeature | DeepFeature Package Skip to the content.
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