The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

IFS curves of two classification algorithms based on feature list yielded by MCFS. by Zhan Dong Li (12481390)

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

List of ageing-associated genes (STRING identifier and corresponding gene symbol) within imputed genes based on the JenAge Ageing Factor Database for each tissue (brain and liver)... by Konstantina Charmpi (9901053)

Data_Sheet_1_Transcriptome-based selection and validation of optimal reference genes in perirenal adipose developing of goat (Capra hircus).zip by Le Zhao (483824)

The flow chart of harmony search-based OED using a double nested loops. by Wei Zhang (405)

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

IFS performance with different classification algorithms and number of top features in the list yielded by MCFS. by Zhan Dong Li (12481390)

<i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling by Fangzhou Shen (5140994)

“…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx by Bingxin Zhang (13011397)

DataSheet1_SS-RNN: A Strengthened Skip Algorithm for Data Classification Based on Recurrent Neural Networks.pdf by Wenjie Cao (780652)

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP by Liangliang Wang (580395)

“…Tumour mutation burden (TMB) was also analysed in the risk groups. Additionally, gene ontology and gene set variation analysis were used for biological function and pathway exploration. …”

Polynomial and Cauchy mutations with different distribution parameters. by Dmitrii Smirnov (8822324)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”