A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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.

埃及尼罗河三角洲城市化监测

对1972、1984和1990年在尼罗河三角洲获取的Landsat MSS和TM影象(scenes)处理的和协同配准的(coregistered)数字镶嵌图比较表明,城镇增长正危及着埃及农业的生产力。在1972、1984和1990年尼罗河三角洲的城镇面积至少分别占3.6%、4.7%和5.7%,在这18年期间增长了58%。大约一半的增长出现在1984～1990年期间。如果这种趋势继续下去,到2010年埃及将因城市化而丧失农地总面积的12%。尽管事实上城镇增长主要出现在城市周围,但在几千个小村庄周围的增长却对尼罗河三角洲的农业生产构成最大的威胁。1972～1990年间,城市和大村庄的累积增长率为37%,而同期小村庄的累积增长率为77%。

DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification

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.

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM⁺and SPOT-5 Data

This study aims to assess and to evaluate band ratios, brovey and HSV (Hue-Saturation-Value) techniques for discrimination and mapping the basement rock units exposed at Wadi Bulghah area, Saudi Arabia using multispectral Landsat ETM+ and SPOT-5 panchromatic data.?FieldSpec instrument is utilized to collect the spectral data of diorite, marble, gossan and volcanics, the main rock units exposed at the study area. Spectral profile of diorite exhibits very distinguished absorption features around 2.20 μm and 2.35 μm wavelength regions. These absorption features lead to lowering the band ratio values within the band-7 wavelength region. Diorite intrusions appear to have grey and dark grey image signatures on 7/3 and 7/2 band ratio images respectively. On the false color composite ratio image (7/3:R;7/2:G and 5/2:B), diorite, marble, gossan and volcanics have very dark brown, dark blue, white and yellowish brown image signatures respectively. Image fusion between previously mentioned FCC ratio image and high spatial resolution (5 meters) SPOT-5 panchromatic image is carried out by using brovey and HSV transformation methods. Visual and statistical assessment methods prove that HSV fused image yields best image interpretability results rather than brovey image. It improves the spatial resolution of the original FCC ratios image with acceptable spectral preservation.