This example shows how to train a neural network to detect cancer using mass spectrometry data on protein profiles.
Serum proteomic pattern diagnostics can be used to differentiate samples from patients with and without disease. Profile patterns are generated using surface-enhanced laser desorption and ionization (SELDI) protein mass spectrometry. This technology has the potential to improve clinical diagnostics tests for cancer pathologies.
The goal is to build a classifier that can distinguish between cancer and control patients from the mass spectrometry data.
The methodology followed in this example is to select a reduced set of measurements or "features" that can be used to distinguish between cancer and control patients using a classifier. These features are ion intensity levels at specific mass/charge values.
The data used in this example is from the FDA-NCI Clinical Proteomics Program Databank: https://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp
To recreate the data in
ovarian_dataset.mat used in this example, download and decompress the raw mass spectrometry data from the FDA-NCI website. Create the data file
OvarianCancerQAQCdataset.mat by following the steps in Batch Processing of Spectra Using Sequential and Parallel Computing (Bioinformatics Toolbox). The new file contains the variables
Each column in
Y represents measurements taken from a patient. There are
216 columns in
216 patients, out of which
121 are ovarian cancer patients and
95 are normal patients.
Each row in
Y represents the ion intensity level at a specific mass-charge value indicated in
MZ. There are
15000 mass-charge values in
MZ and each row in
Y represents the ion-intensity levels of the patients at that particular mass-charge value.
grp holds the index information as to which of these samples represent cancer patients and which ones represent normal patients.
An extensive description of this data set can be found in  and .
This task is a typical classification problem where the number of features is much larger than the number of observations but single feature achieves a correct classification. Therefore, the goal is to find a classifier which appropriately learns how to weight multiple features and at the same time produces a generalized mapping which is not over-fitted.
A simple approach for finding significant features is to assume that each M/Z value is independent and compute a two-way t-test.
rankfeatures returns an index to the most significant M/Z values, for instance 100 indices ranked by the absolute value of the test statistic.
To finish recreating the data from
ovarian_dataset.mat, load the
rankfeatures from Bioinformatics Toolbox to choose 100 highest ranked measurements as inputs
ind = rankfeatures(Y,grp,'Criterion','ttest','NumberOfIndices',100); x = Y(ind,:);
Define the targets
t for the two classes as follows:
t = double(strcmp('Cancer',grp)); t = [t; 1-t];
The preprocessing steps from the script and example listed above are intended to demonstrate a representative set of possible preprocessing and feature selection procedures. Using different steps or parameters can lead to different and possibly better results.
[x,t] = ovarian_dataset; whos x t
Name Size Bytes Class Attributes t 2x216 3456 double x 100x216 172800 double
Each column in
x represents one of 216 different patients.
Each row in
x represents the ion intensity level at one of the 100 specific mass-charge values for each patient.
t has two rows with 216 values each of which are either [1;0], indicating a cancer patient, or [0;1] for a normal patient.
Now that you have identified some significant features, you can use this information to classify the cancer and normal samples.
Since the neural network is initialized with random initial weights, the results after training the network vary slightly every time the example is run. To avoid this randomness, the random seed is set to reproduce the same results every time. However, setting the random seed is not necessary for your own applications.
A 1-hidden layer feed forward neural network with 5 hidden layer neurons is created and trained. The input and target samples are automatically divided into training, validation, and test sets. The training set is used to teach the network. Training continues as long as the network continues improving on the validation set. The test set provides an independent measure of the network accuracy.
The input and output have sizes of 0 because the network has not yet been configured to match the input and target data. This configuration happens when you train the network.
net = patternnet(5); view(net)
Now the network is ready to be trained. The samples are automatically divided into training, validation, and test sets. The training set is used to teach the network. Training continues as long as the network continues improving on the validation set. The test set provides an independent measure of network accuracy.
The Neural Network Training Tool shows the network being trained and the algorithms used to train it. It also displays the training state during training and the criteria which stopped training are highlighted in green.
The buttons at the bottom open useful plots which can be opened during and after training. Links next to the algorithm names and plot buttons open documentation on those subjects.
[net,tr] = train(net,x,t);
To see how the network's performance improved during training, either click the "Performance" button in the training tool, or use the
Performance is measured in terms of mean squared error, and shown on a logarithmic scale. It rapidly decreased as the network was trained.
Performance is shown for each of the training, validation, and test sets.
The trained neural network can now be tested with the testing samples we partitioned from the main dataset. The testing data was not used in training in any way and hence provides an "out-of-sample" dataset to test the network on. This gives an estimate of how well the network will perform when tested with data from the real world.
The network outputs are in the range 0-1. Threshold the outputs to obtain 1's and 0's indicating cancer or normal patients, respectively.
testX = x(:,tr.testInd); testT = t(:,tr.testInd); testY = net(testX); testClasses = testY > 0.5
testClasses = 2x32 logical array Columns 1 through 19 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 Columns 20 through 32 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1
One measure of how well the neural network has fit the data is the confusion plot.
The confusion matrix shows the percentages of correct and incorrect classifications. Correct classifications are the green squares on the matrix diagonal. The red squares represent incorrect classifications.
If the network is accurate, then the percentages in the red squares are small, indicating few misclassifications.
If the network is not accurate, then you can try training for a longer time, or training a network with more hidden neurons.
Here are the overall percentages of correct and incorrect classification.
[c,cm] = confusion(testT,testY); fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c)); fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);
Percentage Correct Classification : 84.375000% Percentage Incorrect Classification : 15.625000%
Another measure of how well the neural network has fit data is the receiver operating characteristic plot. This plot shows how the false positive and true positive rates relate as the thresholding of outputs is varied from 0 to 1.
The farther left and up the line is, the fewer false positives need to be accepted in order to get a high true positive rate. The best classifiers have a line going from the bottom left corner, to the top left corner, to the top right corner, or close to that.
Class 1 indicates cancer patients and class 2 indicates normal patients.
This example demonstrates how neural networks can be used as classifiers for cancer detection. To improve classifier performance, you can also try using techniques like principal component analysis for reducing the dimensionality of the data used for neural network training.
 T.P. Conrads, et al., "High-resolution serum proteomic features for ovarian detection", Endocrine-Related Cancer, 11, 2004, pp. 163-178.
 E.F. Petricoin, et al., "Use of proteomic patterns in serum to identify ovarian cancer", Lancet, 359(9306), 2002, pp. 572-577.