Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Screening and validation for plasma biomarkers of nephrotoxicity based on metabolomics in male rats

Currently, drug-induced nephrotoxicity is widespread and seriously affects human health. However, the conventional indexes of renal function lack sensitivity, leading to a delay in the detection of nephrotoxicity. Therefore, we need to identify more sensitive indexes for evaluating nephrotoxicity. In this study, we used gentamicin (100 mg kg-1), etimicin (100 mg kg-1) and amphotericin B (4 mg kg-1) to establish renal injury models in rats, and we collected information using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry in the screening stage. Thirteen nephrotoxicity metabolites were selected after multivariate statistical and integration analyses. Then, we conducted trend analysis to select 5 nephrotoxicity biomarkers [thymidine, LysoPC(16:1), LysoPC(18:4), LysoPC(20:5), and LysoPC(22:5)] whose content changed consistently at different timepoints after drug administration. To verify the sensitivity and specificity of these biomarkers for nephrotoxicity, receiver operating characteristic (ROC) and support vector machine (SVM) analyses were applied. The area under the curve of the 5 biomarkers were 0.806-0.901 at the 95% confidence interval according to the ROC analysis. We used the SVM classified model to verify these biomarkers, and the prediction rate was 95.83%. Therefore, the 5 biomarkers have strong sensitivity and high accuracy; these biomarkers are more sensitive indexes for evaluating renal function to identify nephrotoxicity and initiate prompt treatment.