sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter

Yang, Zhongliang; Wen, Yangliang; Chen, Yumiao

doi:10.3390/s18103296

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…We have developed a novel machine‐learning approach called the gene expression programming‐complex snp interactions (GEP‐CSI) data mining algorithm. This algorithm, an evolution of GEP [ 18 ], leverages linear algebra calculations to handle discrete variables and compute a fitness function score. GEP enables the exploration of an extensive solution space, pinpointing the optimal solution via a natural selection mechanism within the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

Chen,

Zhou,

Zhang

et al. 2024

Cancer Innovation

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BackgroundThe rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.MethodsWe developed a novel machine‐learning approach called GEP‐CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP‐CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.ResultsWe identified and validated 1184 three‐SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs‐related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.ConclusionWe successfully developed an effective deep‐learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.

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Section: Introductionmentioning

confidence: 99%

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

Chen,

Zhou,

Zhang

et al. 2024

Cancer Innovation

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“…Similarly, Deng et al [11] combined GEP with an artificial fish swarm algorithm and found that the combination performed better than the original GEP. Yang et al [12] developed a hybrid Kalman filter GEP to reconstruct drawing and handwriting.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Length Gene Expression Programming

Amar,

Sudiarso,

Kusumawan

2023

IJCDS

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Gene expression programming (GEP) is capable of solving many prediction, classification, and optimization problem effectively. It uses a fixed-length chromosome representing a set of equations. However, the chromosome length significantly affects the algorithm's performance. Different problems may require varied chromosome lengths to achieve good results. Only a few studies have been conducted to deal with chromosome length in GEP. Therefore, this study aimed to develop an adaptive GEP to find proper chromosome length during the evolutionary process. The study proposed that the chromosome length may be varied for each individual instead of using the same length in the population. The evolutionary process would adjust the chromosome length and the chromosome with proper length will tend to survive. Furthermore, the study proposed a contraction operator that could delete or insert an allele in the chromosome to make it short or extended. This operator is expected to adjust the chromosome length to its optimal. A special slice crossover was also proposed to accommodate the crossover between parents with different chromosome lengths. The proposed algorithms' performance was investigated by solving three symbolic regression problems. Additionally, the performance was compared to related previous gene expression programming algorithms.

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sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter

Cited by 2 publications

References 28 publications

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

Adaptive Length Gene Expression Programming

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