Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization.

Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.

An efficient rotational direction heap-based optimization with orthogonal structure for medical diagnosis.

The heap-based optimizer (HBO) is an optimization method proposed in recent years that may face local stagnation problems and show slow convergence speed due to the lack of detailed analysis of optimal solutions and a comprehensive search. Therefore, to mitigate these drawbacks and strengthen the performance of the algorithm in the field of medical diagnosis, a new MGOHBO method is proposed by introducing the modified Rosenbrock's rotational direction method (MRM), an operator from the grey wolf optimizer (GWM), and an orthogonal learning strategy (OL). The MGOHBO is compared with eleven famous and improved optimizers on IEEE CEC 2017. The results on benchmark functions indicate that the boosted MGOHBO has several significant advantages in terms of convergence accuracy and speed of the process. Additionally, this article analyzed the diversity and balance of MGOHBO in detail. Finally, the proposed MGOHBO algorithm is utilized to optimize the kernel extreme learning machines (KELM), and a new MGOHBO-KELM is proposed. To validate the performance of MGOHBO-KELM, seven disease diagnostic questions were introduced for testing in this work. In contrast to advanced models such as HBO-KELM and BP, it can be concluded that the MGOHBO-KELM model can achieve optimal results, which also proves that it has practical significance in solving medical diagnosis problems.

Epigenetic silencing of DUSP9 induces the proliferation of human gastric cancer by activating JNK signaling.

Dual-specificity phosphatase 9 (DUSP9) is a strong negative regulator of transcription factor activating kinases (ERK, JNK and p38) in the mitogen-activated protein kinase (MAPK) pathways. The aim of this study was to examine the CpG island methylation status of DUSP9 using bisulfite sequencing PCR (BSP) in gastric cancer (GC). The investigation was conducted on 30 clinical GC samples and selected corresponding tumor-free normal gastric mucosa tissues, using BSP for the determination of the promoter methylation status. The methylation status of the tumor samples was compared to the corresponding tumor-free samples. DUSP9 was silenced by promoter region hypermethylation and G2/M phase arrest was induced by DUSP9 in the MKN-1 GC cell line. MKN-1 proliferation was suppressed by DUSP9 by inhibiting c-Jun, which was induced by JNK signaling. The expression levels of CCND1, c-Jun, CDK4 and CDK6 were upregulated while p21 was downregulated by DUSP9 in MKN-1 cells. However, DUSP9-induced resulted in the regulation of the levels of cycle-related molecules, whivh were inhibited when the JNK inhibitor SP600125 was added. In conclusion, DUSP9 was frequently methylated in human GC and the expression of DUSP9 is silenced by promoter region hypermethylation. The results of this study, combined with previous studies, suggested that therapeutic intervention to increase the expression or activity of DUSP9 may enable the activation of anti-proliferation signals in malignant cells.