3篇
您的检索式：作者名="Rubiyah Yusof"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Adaptive neural-fuzzy control system by RBF and GRNN neural networks显示文摘 | Seng Teo Lian Khalid Marzuki Yusof Rubiyah Omatu Sigeru | 1998 | Journal of Intelligent and Robotic Systems1998，23，： | 1 |
| 2 | A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer显示文摘Epigenetics is the study of phenotypic variations that do not alter DNA sequences.Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers.One of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer.Therefore,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences.Currently,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying loci.At each DNA methylation site,a beta value(β)is used to reflect the methylation intensity.Therefore,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user bias.This study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites.The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research Institute.The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data sets.The NBDC-GA outperformed the IGA in terms of convergence performance.Furthermore,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,respectively.The proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities that were previously linked to cancer. | Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 3 | DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification显示文摘The identification of DNA binding proteins(DNABPs)is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g.,in the study of the biological,biophysical,and biochemical effects of antibiotics,drugs,and steroids on DNA.This paper presents an efficient approach for DNABPs identification based on deep transfer learning,named“DTLM-DBP.”Two transfer learning methods are used in the identification process.The first is based on the pre-trained deep learning model as a feature’s extractor and classifier.Two different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and compared.The second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification process.Two classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and compared.The proposed approach is tested using different DNA proteins datasets.The performance of the identification process is evaluated in terms of identification accuracy,sensitivity,specificity and MCC,with four available DNA proteins datasets:PDB1075,PDB186,PDNA-543,and PDNA-316.The results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest performance.DTLM-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification. | Sara Saber Uswah Khairuddin Rubiyah Yusof Ahmed Madani | 2021 | Computers, Materials & Continua2021，，9： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员