Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered considerable improvements in the precision of solutions that use ML, such as Deep Learning (DL). In this study, we compare the most widely used algorithms in classical ML and DL to classify the tumors described in the 11_tumor database. We obtained tumor identification accuracies between 90.6% (Logistic Regression) and 94.43% (Convolutional Neural Networks) using k-fold cross-validation. Also, we show how a tuning process may or may not significantly improve algorithms’ accuracies. Our results demonstrate an efficient and accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates tumor type prediction in a multi-cancer-type scenario.
Machine Learning, Deep Learning, Cancer classification, Microarray gene expression, 11_tumor database, Bioinformatics
Cancer is one of the most deadly diseases in human health caused by the abnormal proliferation of cells, leading to malignant malformations or tumors with different pathology characteristics (Varadhachary, 2007). Cancer-type classification is critical to increasing patient survival rates. Molecular genetic analyses have discovered genetic alterations, or signatures, with different biological characteristics that allow discerning the responses to several treatments (Greller & Tobin, 1999). This enables early diagnosis and an accurate treatment; therefore, ensuring the efficacy and reduction of side effects (toxicity) of the treatment (Wang et al., 2005).
Impaired gene expression is a characteristic of carcinogenic cells (Su et al., 2001). Accordingly, microarray gene expression data from tumor cells provide an important source of information to improve cancer diagnosis in a cost-efficient manner, allowing the use of this strategy in developing countries. Since microarray datasets contain thousands of different genes to be analyzed, an accurate and efficient way of analyzing this amount of data is by Machine Learning (ML) and Deep Learning (DL) algorithms (Motieghader et al., 2017). In particular, these algorithms have been applied in other biological areas, including rules of association (Orozco-Arias et al., 2019b). Previous studies demonstrate the use of ML and DL in microarray gene expression to infer the expression of target genes based on landmark gene expression (Chen et al., 2016), in feature selection aimed at finding an informative subset of gene expression (Sharma, Imoto & Miyano, 2012), and in the diagnosis and classification of cancer types (Fakoor et al., 2013).
A well-known database of gene microarrays related to cancer is the 11_Tumors database (Su et al., 2001), which is available at https://github.com/simonorozcoarias/ML_DL_ microArrays/blob/master/data11tumors2.csv. This dataset is a good example of the curse of dimensionality due to the high number of characteristics and few registers of this database. Therefore, most studies use it to test specific data science techniques, such as feature selection methods (Bolón-Canedo et al., 2014; Wang & Wei, 2017; Han & Kim, 2018; Perera, Chan & Karunasekera, 2018), dimension reduction (Araújo et al., 2011), clustering methods (Sardana & Agrawal, 2012; Sirinukunwattana et al., 2013; Li et al., 2017), preprocessing techniques (Liu et al., 2019), among others. The 11_Tumors database has also been used in gene selection for cancer classification (Moosa et al., 2016; Alanni et al., 2019). Although the authors achieved high accuracy in these publications, they only used some ML algorithms, one preprocessing strategy, and one learning technique (supervised or unsupervised), which could add bias to their methodology. Additionally, to date, no comparative study on the application of ML in microarray datasets is found in the literature.
In several ML studies, DL has proven to be a robust technique for analyzing large-scale datasets (Bengio, Courville & Vincent, 2013). With these advances, DL has achieved cutting-edge performance in a wide range of applications, including bioinformatics and genomics (Min, Lee & Yoon, 2016; Yue & Wang, 2018), analysis of metagenomics samples (Ceballos et al., 2019), identification of somatic transposable elements in ovarian cancer (Tang et al., 2017), identification and classification of retrotransposons in plants (Orozco-Arias, Isaza & Guyot, 2019) and cancer classification using Principal Component Analysis (PCA) (Liu, Cai & Shao, 2011). Recent work by Guillen & Ebalunode (2016) demonstrated promising results for the application of DL in microarray gene expression.
In general, there are two different tasks that ML algorithms can tackle: supervised and unsupervised learning. In supervised learning, the goal is to predict the label (classification) or response (regression) of each data point by using a provided set of labeled training examples. In unsupervised learning, such as clustering and principal component analysis, the goal is to learn inherent patterns within the data (Zou et al., 2018).
The main goal of any ML task is to optimize model performance not only on the training data but also on additional datasets. When a learned model displays this behavior, it is considered to generalize well. With this aim, the data in a given database are randomly split into at least two subsets: training and validation (Zou et al., 2018). Then, a model as complex as possible is learned (training set), tuned (validation set), and tested for generalization performance on the validation set. This process is crucial for avoiding overfitting or under-fitting. Therefore, a sound learning algorithm must reach an appropriate balance between model flexibility and the amount of training data. An overly simple model will under fit and make inadequate predictions, whereas an overly flexible model will overfit to spurious patterns in the training data and not generalize (Zou et al., 2018).
In this study, we compare the performance of the most commonly used ML and DL algorithms in bioinformatics (Orozco-Arias et al., 2019a) in the task of classifying by supervised and unsupervised techniques. We used the 11_Tumor database and applied different preprocessing strategies. Our detailed evaluation and comparison illustrate the high accuracy of these algorithms for tumor identification in a multiple-cancer-type scenario and the influence of preprocessing strategies and tuning processes on these accuracies.
Cancer is predicted to become the most deadly disease for humans in the future (Dagenais et al., 2019); therefore, early diagnosis, identification, and treatment are needed to control the disease. ML and DL techniques are promising tools for the classification of cancer types using complex datasets, such as microarrays. In this study, we obtained predictions with as high as 93.52% and 94.46% accuracies, which will allow patients with these types of pathologies to receive an early and precise detection of their disease, and will also contribute to the discovery of new selective drugs for the treatment of these types of tumors.
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FULL Paper PDF file:A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data
Reinel Tabares-Soto1 , Simon Orozco-Arias, Victor Romero-Cano , Vanesa Segovia Bucheli , José Luis Rodríguez-Sotelo and Cristian Felipe Jiménez-Varón
A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data
PeerJ Computer Science