An enhance multimodal multiobjective optimization genetic algorithm with special crowding distance for pulmonary hypertension feature selection

Analyzing the molecular mechanism of xuefuzhuyu decoction in the treatment of pulmonary hypertension with network pharmacology and bioinformatics and verifying molecular docking

BACKGROUND

XueFuZhuYu (XFZY), a typical Chinese herbal formula, has remarkable clinical effects for treating Pulmonary Hypertension (PH) with unclear mechanisms. Our research involved the utilization of network pharmacology to explore the traditional Chinese herbal monomers and their related targets within XFZY for PH treatment. Furthermore, molecular docking verification was performed.

METHODS

The XFZY's primary active compounds, along with their corresponding targets, were both obtained from the TCMSP, ChEMBL, and UniProt databases. The target proteins relevant to PH were sifted through OMIM, GeneCards and TTD databases. The common "XFZY-PH" targets were evaluated with Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses with the assistance of R software. The Protein-Protein Interaction (PPI) network and compound-target-pathway network were constructed and a systematic analysis of network parameters was performed by the powerful software Cytoscape. Molecular docking was employed for assessing and verifying the interactions between the core targets and the top Chinese herbal monomer.

RESULTS

The screening included 297 targets of active compounds in XFZY and 8400 PH-related targets. DO analysis of the above common 268 targets indicated that the treatment of the diseases by XFZY is mediated by genes related to Chronic Obstructive Pulmonary Disease (COPD), Obstructive Lung Disease (OLD), ischemia, and myocardial infarction. The findings from molecular docking indicated that the binding energies of 57 ligand-receptor pairs in PH and 20 ligand-receptor pairs in COPD-PH were lower than -7kJ•mol-1.

CONCLUSIONS

This study indicates that XFZY is a promising option within traditional Chinese medicine compound preparation for combating PH, particularly in cases associated with COPD. Our demonstration of the specific molecular mechanism of XFZY anti-PH and its effective active ingredients provides a theoretical basis for better clinical application of the compound.

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance

Background

Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal.

Methods

In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features.

Results

The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.

A feature selection framework for anxiety disorder analysis using a novel multiview harris hawk optimization algorithm

Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL2), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.

Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

Real-world optimization problems require some advanced metaheuristic algorithms, which functionally sustain a variety of solutions and technically explore the tracking space to find the global optimal solution or optimizer. One such algorithm is the newly developed COOT algorithm that is used to solve complex optimization problems. However, like other swarm intelligence algorithms, the COOT algorithm also faces the issues of low diversity, slow iteration speed, and stagnation in local optimization. In order to ameliorate these deficiencies, an improved population-initialized COOT algorithm named COBHCOOT is developed by integrating chaos map, opposition-based learning strategy and hunting strategy, which are used to accelerate the global convergence speed and boost the exploration efficiency and solution quality of the algorithm. To validate the dominance of the proposed COBHCOOT, it is compared with the original COOT algorithm and the well-known natural heuristic optimization algorithm on the recognized CEC2017 and CEC2019 benchmark suites, respectively. For the 29 CEC2017 problems, COBHCOOT performed the best in 15 (51.72%, 30-Dim), 14 (48.28%, 50-Dim) and 11 (37.93%, 100-Dim) respectively, and for the 10 CEC2019 benchmark functions, COBHCOOT performed the best in 7 of them. Furthermore, the practicability and potential of COBHCOOT are also highlighted by solving two engineering optimization problems and four truss structure optimization problems. Eventually, to examine the validity and performance of COBHCOOT for medical feature selection, eight medical datasets are used as benchmarks to compare with other superior methods in terms of average accuracy and number of features. Particularly, COBHCOOT is applied to the feature selection of cervical cancer behavior risk dataset. The findings testified that COBHCOOT achieves better accuracy with a minimal number of features compared with the comparison methods.