Cancer Detection based on Microarray Data Classification Using FLNN and Hybrid Feature Selection

Cancer is one of the second deadliest diseases in the world after heart disease. Citing from the WHO's report on cancer, in 2018 there were around 18.1 million cases of cancer in the world with a total of 9.6 million deaths. Now that bioinformatics technology is growing and based on WHO’s report on cancer, an early detection is needed where bioinformatics technology can be used to diagnose cancer and to help to reduce the number of deaths from cancer by immediately treating the person. Microarray DNA data as one of the bioinformatics technology is becoming popular for use in the analysis and diagnosis of cancer in the medical world. Microarray DNA data has a very large number of genes, so a dimensional reduction method is needed to reduce the use of features for the classification process by selecting the most influential features. After the most influential features are selected, these features are going to be used for the classification and predict whether a person has cancer or not. In this research, hybridization is carried out by combining Information Gain as a filtering method and Genetic Algorithm as a wrapping method to reduce dimensions, and lastly FLNN as a classification method. The test results get colon cancer data to get the highest accuracy value of 90.26%, breast cancer by 85.63%, lung cancer and ovarian cancer by 100%, and prostate cancer by 94.10%.

Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.

Logistic Broken Adaptive Ridge Procedure for Colon Cancer Data Analysis

Background: Colon cancer is the leading cause of cancer-related deaths in the world in both man and women. Knowing the causes and risk factors for colon cancer can help you understanding the importance of routine screening for colon cancer, as well as learn if you are one of the people who should begin screening at the earlier age. Due to the limitation of clinical diagnose, management and treatment outcomes, it is of great necessity to develop effective methods for colon cancer detection and prediction especially cDNA Microarrays and high- density oligonucleotide chips are increasingly used in cancer research. Methods: Here we propose a novel logistic broken adaptive ridge procedure to address the problem of colon cancer results prediction through selecting effective few variables or genes from 2000 candidate genes. Results: In total 62 cases with 40 colon cancer patients and 22 healthy patients were included in our analysis. Each case consists of 2000 genes which challenged all the competitive method. From the results, we are so surprised that our proposed method outperforms the classical variable selection approaches in error rate of training data and extra testing data. Conclusions: Logistic adaptive ridge procedure is very effective for colon cancer predictions, either in terms of prognosis or diagnose. It may benefit patients by guiding therapeutic options. We hope it will contribute to the wider biology and related communities.

Principal Component Analysis Sebagai Ekstraksi Fitur Data Microarray Untuk Deteksi Kanker Berbasis Linear Discriminant Analysis

Cancer is one of the leading causes of death globally. Early detection of cancer allows better treatment for patients. One method to detect cancer is using microarray data classification. However, microarray data has high dimensions which complicates the classification process. Linear Discriminant Analysis is a classification technique which is easy to implement and has good accuracy. However, Linear Discriminant Analysis has difficulty in handling high dimensional data. Therefore, Principal Component Analysis, a feature extraction technique is used to optimize Linear Discriminant Analysis performance. Based on the results of the study, it was found that usage of Principal Component Analysis increases the accuracy of up to 29.04% and f-1 score by 64.28% for colon cancer data.

Genotype patterns in growing solid tumors

Over the past decade, the theory of tumor evolution has largely focused on the selective sweeps model. According to this theory, tumors evolve by a succession of clonal expansions that are initiated by driver mutations. In a 2015 analysis of colon cancer data, Sottoriva et al [34] proposed an alternative theory of tumor evolution, the so-called Big Bang model, in which one or more driver mutations are acquired by the founder gland, and the evolutionary dynamics within the expanding population are predominantly neutral. In this paper we will describe a simple mathematical model that reproduces qualitative features of the observed paatterns of genetic variability and makes quantitative predictions.