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Microarrays provide a new technique of measuring gene expression that attracted a lot of research interest in recent years. It has been suggested that gene expression data from microarrays (biochips) can be utilized in many biomedical areas, for example in cancer classification. Whereas several, new and existing, methods of classification has been tested, a selection of proper (optimal) set of genes, which expression serves during classification, is still an open problem. In this paper we propose a heuristic method of choosing suboptimal set of genes by using support vector machines (SVM). Obtained set of genes optimizes leave-one-out cross-validation error. The method is tested on microarray gene expression data of samples of two cancer types: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). The results show that quality of classification is much better than for sets obtained using other methods of feature selection. In addition, we demonstrate that maximum separation in a training data set may lead to deterioration of performance in an independent validation data set, a phenomenon akin to overfitting.

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Over the past years applications of fusion technique have been growing rapidly. However, very few applications of the technique to microarray data have been reported. In this paper, we propose a new fusion method based on pseudo standard deviation minimization (PSDM) for the feature selection of microarray. This new method provides a more accurate set of features. Therefore the classification can be performed and functional meaning from the features can also be revealed. The new method is actually obtained through a combination of two different feature selection methods (FSMs). It is shown that it can explore nonperfect correlation between gene expression profile and cancer classes or feature detection algorithms. To evaluate its effectiveness, it is tested on lymphoma and leukemia microarray expression datasets and then compared with the existing methods. Self-organizing map (SOM) is used for feature classification. It can be seen through the comparison that the classification accuracy of the new fusion method is at least 2% ~ 3% higher than others.

Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients’ survival. Since disease developments involve complex interplay among multiple factors such as gene–gene interactions, cancer classifications based on microarray gene expression profiling data are expected to be effective, and hence, have attracted extensive attention in computational biology and medicine. However, when using genomic data to build a diagnostic model, there exist several problems to be overcome, including the high-dimensional feature space and feature contamination. In this paper, we propose using the overlapping group screening (OGS) approach to build an accurate cancer diagnosis model and predict the probability of a patient falling into some disease classification category in the logistic regression framework. This new proposal integrates gene pathway information into the procedure for identifying genes and gene–gene interactions associated with the classification of cancer outcome groups. We conduct a series of simulation studies to compare the predictive accuracy of our proposed method for cancer diagnosis with some existing machine learning methods, and find the better performances of the former method. We apply the proposed method to the genomic data of The Cancer Genome Atlas related to lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LHC), and thyroid carcinoma (THCA), to establish accurate cancer diagnosis models.

Biotechnological analysis of DNA microarray genes provides valuable insights into the discovery and treatment of diseases such as cancer. It may also be crucial for the prevention and treatment of other genetic diseases. However, due to the large number of features and dimensions in a DNA microarray, the “curse of dimensions” problem is very common. Many machine learning methods require an effective subset of input genes to achieve high accuracy. Unfortunately, extracting features (genes) is an inherently NP-hard problem. Recently, the use of metaheuristics to overcome the NP-hardness of the feature extraction problem has attracted the attention of many researchers. In this paper, we use the combination of fuzzy entropy and Giza Pyramid Construction (GPC) for feature selection. First, redundant features in the microarray dataset are removed using the fuzzy entropy approach. GPC is then used to reduce the execution time. This results in the selection of a near-optimal subset of genes for cancer detection. Dimensionality reduction with GPC followed by classification with Convolutional Neural Network (CNN) creates a synergy to increase efficiency. The proposed method is tested on five well-known cancer patient datasets: leukemia, lymphoma, MLL, ovarian, and SRBCT. The performance of CNN was also measured with four well-known classifiers, including K-nearest neighbor, naïve Bayesian, decision tree, and logistic regression. Our results show that, on average, CNN has the highest accuracy, recall, precision, and F-measure in all datasets.

This discussion about diagnostic tests for cancer incorporates a powerful branch of Physics namely X-ray diffraction. Although this technique was used to solve the DNA structure using the X-ray diffraction pictures of Rosalind Franklin,¹ and the structure of vitamin B12 by Dorothy Hodgkin² and hosts of other medical related structures, it is poorly understood by the general medical profession and the community at large. To the nonphysicist the patterns appear to have no relation to the results produced. It might as well be written in Greek. The well-known quote of Poincaré, the famous French mathematician and scientist, in 1885 comes to mind: "Science is built up with facts as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house."

In order therefore to build a true understanding of this powerful technique it is necessary to build a firm understanding of the basic facts about this technique, so that the final results will be clear to all, as they will be held up by a firm house of knowledge. So let us take up the first stone.

Papillary thyroid carcinoma (PTC) is typically an indolent cancer, yet a minority of cases develop lymph node metastasis. Due to the unclear mechanisms of lymph node metastasis, a considerable number of patients undergo unnecessary surgeries. Currently, the identification of key genetic biomarkers in high-dimensional data presents a significant challenge, thereby limiting research progress in this area. Here, we proposed a hybrid filter-wrapper feature selection strategy for core factor detection and developed MethyAE, a metastasis prediction model based on DNA methylation, utilizing an end-to-end learning auto-encoder. 46 methylated CpG sites were successfully identified as crucial biomarkers for lymph node metastasis. Leveraging 447 PTC samples from the Cancer Genome Atlas (221 with metastasis, 226 without), the MethyAE model achieves 88.9% accuracy and a recall rate of 88.6% in predicting lymph node metastasis, outperforming commonly used machine learning methods like logistic regression and random forest. Furthermore, the MethyAE model exhibits favorable performance in DNA methylation data from colon cancer, bladder cancer, and breast cancer. To the best of our knowledge, this is the first attempt to predict PTC lymph node metastasis through DNA methylation, offering pivotal decision-making criteria for avoiding unnecessary surgeries and selecting appropriate treatment plans for a substantial cohort of PTC patients.