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Adaptive bio-inspired gene optimisation based deep neural associative classification for diabetic disease diagnosis 基于自适应仿生基因优化的深度神经关联分类在糖尿病疾病诊断中的应用
Int. J. Bioinform. Res. Appl. Pub Date : 2021-08-13 DOI: 10.1504/ijbra.2021.117168
D. Sasirekha, A. Punitha
{"title":"Adaptive bio-inspired gene optimisation based deep neural associative classification for diabetic disease diagnosis","authors":"D. Sasirekha, A. Punitha","doi":"10.1504/ijbra.2021.117168","DOIUrl":"https://doi.org/10.1504/ijbra.2021.117168","url":null,"abstract":"Associative classification plays a significant role in data mining. With several classification techniques being used, the accuracy with which classification was performed was found to be inadequate. To overcome this issue, an adaptive bio-inspired gene optimisation based deep neural associative classification (ABGO-DNAC) technique is proposed. ABGO-DNAC technique generates association rules with the minimal number of medical attributes by applying ABGO algorithm and choosing optimal attributes from the medical dataset. With formulated association rules, Gaussian deep feed forward neural learning (GDFNL) is designed for diabetic disease classification. GDFNL deeply analyses the patient's medical data and classify patients as normal or abnormal. Simulation evaluation of ABGO-DNAC technique is performed on disease prediction accuracy, disease prediction time and false positive rate with different patients. Simulation results depict ABGO-DNAC technique disease prediction accuracy and also reduce diabetic disease diagnosing as compared to state-of-the-art works.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"60 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114131642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
New gene selection algorithm using hypeboxes to improve performance of classifiers 新基因选择算法使用超盒来提高分类器的性能
Int. J. Bioinform. Res. Appl. Pub Date : 2020-08-14 DOI: 10.1504/ijbra.2020.10031329
A. Bagirov, Karim Mardaneh
{"title":"New gene selection algorithm using hypeboxes to improve performance of classifiers","authors":"A. Bagirov, Karim Mardaneh","doi":"10.1504/ijbra.2020.10031329","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10031329","url":null,"abstract":"The use of DNA microarray technology allows to measure the expression levels of thousands of genes in one single experiment which makes possible to apply classification techniques to classify tumours. However, the large number of genes and relatively small number of tumours in gene expression datasets may (and in some cases significantly) diminish the accuracy of many classifiers. Therefore, efficient gene selection algorithms are required to identify most informative genes or groups of genes to improve the performance of classifiers. In this paper, a new gene selection algorithm is developed using marginal hyberboxes of genes or groups of genes for each tumour type. Informative genes are defined using overlaps between hyberboxes. The results on six gene expression datasets demonstrate that the proposed algorithm is able to considerably reduce the number of genes and significantly improve the performance of classifiers.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124634614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SCAN DB: an integrated catalogue of computationally characterised NER specific skin cancers SCAN DB:计算表征NER特异性皮肤癌的综合目录
Int. J. Bioinform. Res. Appl. Pub Date : 2020-08-14 DOI: 10.1504/ijbra.2020.10031326
Varsha Mehta, Tanya Singh, Ankush Bansal, T. Singh
{"title":"SCAN DB: an integrated catalogue of computationally characterised NER specific skin cancers","authors":"Varsha Mehta, Tanya Singh, Ankush Bansal, T. Singh","doi":"10.1504/ijbra.2020.10031326","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10031326","url":null,"abstract":"SCAN DB, acronym for Skin cancer NER database, provides a unique, first of its kind repository for understanding the regulatory mechanism of the nucleotide excision repair (NER) pathway, disease dynamics, genetics, clinical information, expression, and evolutionary trajectories of the skin cancers. DNA damage has emerged as a major culprit in cancer and many age related diseases. DNA repair and genomic integrity management have become of prime importance in this cancerous era. One of the significant pathways to remove these bulky lesions is NER pathway, whose deficiencies of NER repair proteins are also associated with the skin cancer prone inherited disorder - Xeroderma pigmentosum and other neurodegenerative abnormalities like Cockayne Syndrome and Trichothiodystrophy. However, a well structured, integrated and comprehensive resource of NER pathway and related skin cancers is presently not available. Therefore, SCAN DB effectively bridges this gap in knowledge. The database can be accessed using the URL: http://bioinfoindia.org/SCANDB//index.php","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127940011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A concept of sub-bands event related potentials to increase classes of brain computer interface system 子带事件相关电位的概念增加了脑机接口系统的分类
Int. J. Bioinform. Res. Appl. Pub Date : 2020-08-14 DOI: 10.1504/ijbra.2020.10031328
M. K. Ahirwal, Anil Kumar, G. Singh
{"title":"A concept of sub-bands event related potentials to increase classes of brain computer interface system","authors":"M. K. Ahirwal, Anil Kumar, G. Singh","doi":"10.1504/ijbra.2020.10031328","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10031328","url":null,"abstract":"Event related potential (ERP) based brain computer interfacing (BCI) achieves significant stability. Changes in electroencephalogram (EEG) signals related to various tasks have been significantly observed. However, each ERP related to particular task can be only exploited as one-to-one relation with specific command or operation. This limits the variability of BCI system and increases the amount of work to identify task related accurate pattern changes in EEG. In this paper, sub-band analysis of detected ERP is proposed in order to factorise one-to-one relation into one-to-many for increasing the variability of BCI system. First, the hypothesis based on analysis of event related spectral perturbation (ERSP) is stated, and then the hypothetical concept is generalised with sub-bands decomposition of ERP, followed by culminative power estimation. Results show that the proposed technique can be easily implemented as a method of combined factorised feature extraction (CFFE) to execute multiple commands from single ERP.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122934167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Usage of ensemble model and genetic algorithm in pipeline for feature selection from cancer microarray data 集成模型和遗传算法在流水线中的应用于癌症微阵列数据的特征选择
Int. J. Bioinform. Res. Appl. Pub Date : 2020-08-14 DOI: 10.1504/ijbra.2020.10031327
Sahu Barnali, Satchidananda Dehuri, A. Jagadev
{"title":"Usage of ensemble model and genetic algorithm in pipeline for feature selection from cancer microarray data","authors":"Sahu Barnali, Satchidananda Dehuri, A. Jagadev","doi":"10.1504/ijbra.2020.10031327","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10031327","url":null,"abstract":"This paper proposes an ensemble of feature selection techniques with genetic algorithm (GA) in pipeline for selecting features from microarray data. The ensemble is a combination of filter and wrapper-based feature selection methods. In addition, GA in pipeline has been used for refinement of ensemble output to produce a non-local set of robust feature subset. An extensive computational experiment has been carried out on a prostate cancer dataset for validation of the method and comparison with group genetic algorithm (GGA). Finally, the resultant feature subsets of GA, GGA, and other constituents of the ensemble in standalone mode have been used for uncovering frequent patterns based on Apriori and FP-growth. The experimental study confirms that the proposed method gives classification accuracy of 100%, 98.34%, 98.02%, and 97% based on an ensemble of classifiers w. r. t. 5, 10, 15, and 20 features, respectively, vis-a-vis 92.34%, 90.34%, 86.54%, and 87.21% of GGA.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
An empirical study of the big data classification methodologies 大数据分类方法的实证研究
Int. J. Bioinform. Res. Appl. Pub Date : 2020-07-13 DOI: 10.1504/ijbra.2020.10030398
S. Mujeeb, R. Sam, Madhavi Kasa
{"title":"An empirical study of the big data classification methodologies","authors":"S. Mujeeb, R. Sam, Madhavi Kasa","doi":"10.1504/ijbra.2020.10030398","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10030398","url":null,"abstract":"The two hasty emanating technologies are big data and cloud computing. Cloud computing is a novel archetype for providing the computing environment in contrast the big data processing technology is convenient for most of the resource types. Now, a productive cloud-based methodology must be devised for the effective management of the big data. This survey presents the distinct cloud-based classification and clustering approaches adopted for the effective big data classification. This paper reviews 40 research papers in the field of big data classification methodologies, like fuzzy classifier, Bayesian model, support vector machine (SVM) classifier, K-means clustering, collaborative filtering based clustering and so on. Moreover, an elaborative analysis and discussion are made by concerning the employed methodology, evaluation metrics, accuracy range, adopted framework, datasets utilised and the implementation tool. Eventually, the research gaps and issues of various conventional cloud-based big data classification schemes are presented for extending the researchers towards a better contribution of significant big data management.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122429890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Diagnosis of abdominal mass in ultrasound images using linear collaborative discriminant regression classification 应用线性协同判别回归分类诊断超声图像中的腹部肿块
Int. J. Bioinform. Res. Appl. Pub Date : 2020-07-13 DOI: 10.1504/ijbra.2020.10030407
S. Kore, Ankush B. Kadam
{"title":"Diagnosis of abdominal mass in ultrasound images using linear collaborative discriminant regression classification","authors":"S. Kore, Ankush B. Kadam","doi":"10.1504/ijbra.2020.10030407","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10030407","url":null,"abstract":"An abdominal ultrasound image is a practical way of checking internal organs. This paper intends to develop an advanced model for diagnosing abdominal masses using US images. This detection technique is accomplished in two stages including Feature extraction and Classification. During feature extraction, texture feature is extracted from US image by adaptive gradient location and orientation histogram (AGLOH). Later in the classification stage, linear collaborative discriminant regression classification (LCDRC) model is used to classify whether the image is normal or abnormal. The classification error produced by the collaborative demonstration is lesser when evaluated with the error produced by the demonstration of single class. Therefore, an improved diagnosis precision is achieved. The features of the proposed AGLOH method are compared with conventional techniques such as gradient location and orientation histogram (GLOH). Further, the classifier of the proposed LCDRC method is compared with conventional techniques such as SVM and NN and validates the effectiveness of the proposed method.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133258496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An exhaustive study on the lung cancer risk models 对肺癌风险模型的详尽研究
Int. J. Bioinform. Res. Appl. Pub Date : 2020-07-05 DOI: 10.1504/ijbra.2020.10030366
M. Shanid, A. Anitha
{"title":"An exhaustive study on the lung cancer risk models","authors":"M. Shanid, A. Anitha","doi":"10.1504/ijbra.2020.10030366","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10030366","url":null,"abstract":"One of the critical cancers leading to an upsurging rate of mortality is lung cancer. The Computed Tomography (CT) is the vastly adopted technique for effective cancer detection and risk assessment. The mortality rate and the intrusive surgery can be reduced through the risk assessment of cancer at the earlier stages. Hence, an essential lung cancer detection technique must be modelled for the risk assessment of cancer at the earlier stages. This review paper is made by carrying out a detailed survey on 40 research works presenting the existing lung cancer detection methodologies. Also extensive analysis and discussion is made with respect to the publication year, adopted detection schemes, evaluation metrics, utilised datasets, a simulation tool, accuracy range, and the extracted features. Subsequently, the research gaps and issues of the distinct lung cancer detection schemes are elucidated for directing the researchers to a better contribution of effective cancer risk assessment.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intelligent model for diabetic retinopathy diagnosis: a hybridised approach 糖尿病视网膜病变诊断的智能模型:一种混合方法
Int. J. Bioinform. Res. Appl. Pub Date : 2020-07-05 DOI: 10.1504/ijbra.2020.10030363
S. Randive, R. K. Senapati, A. Rahulkar
{"title":"Intelligent model for diabetic retinopathy diagnosis: a hybridised approach","authors":"S. Randive, R. K. Senapati, A. Rahulkar","doi":"10.1504/ijbra.2020.10030363","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10030363","url":null,"abstract":"As diabetic retinopathy (DR) is considered as most common infectious diseases in humans, more researches have been already proposed under various aspects, yet the attainment of accurate DR detection seems to be an issue. This paper intends to develop an innovative contribution by introducing a novel DR detection model, and further the proposed model tells the severity of retinopathy from the given input fundus image. The proposed model comprises of stages such as Segmentation, Feature Extraction and Classification. Here, Active contour model is used for segmentation; also the GLCM and GLRM features are extracted during feature extraction process. Moreover, the classifier called neural network (NN) is used for classification purpose. As a main contribution, the extracted features (feature selection), and weight in NN model are optimally chosen by a new hybridised algorithm called whale with particle swarm optimisation (WP), which compares its performance over other conventional methods for analysis purpose.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel 使用长寿家族研究基因型和序列数据与1000基因组参考面板的遗传插入方法的比较
Int. J. Bioinform. Res. Appl. Pub Date : 2020-02-02 DOI: 10.1504/ijbra.2020.10026541
A. Kraja, E. W. Daw, P. Lenzini, Lihua Wang, Shiow J. Lin, Christine A. Williams, Alan B. Wells, K. Lunetta, J. Murabito, P. Sebastiani, G. Tosto, S. Barral, R. Minster, A. Yashin, T. Perls, M. Province
{"title":"A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel","authors":"A. Kraja, E. W. Daw, P. Lenzini, Lihua Wang, Shiow J. Lin, Christine A. Williams, Alan B. Wells, K. Lunetta, J. Murabito, P. Sebastiani, G. Tosto, S. Barral, R. Minster, A. Yashin, T. Perls, M. Province","doi":"10.1504/ijbra.2020.10026541","DOIUrl":"https://doi.org/10.1504/ijbra.2020.10026541","url":null,"abstract":"This study compares methods of imputing genetic markers, given a typed GWAS scaffold from the Long Life Family Study (LLFS) and latest reference panel of 1000-Genomes. We examined two programs for pre-phasing haplotypes MACH/SHAPEIT2 and MINIMAC/IMPUTE2 for imputation. SHAPEIT2 is advantageous for haplotype pre-phasing. MINIMAC and IMPUTE2 produced similar imputation quality. We used a 4MB region on chromosome 2 of LLFS and in the Supplement, we compared methods using chromosome 19 data from the Genetic Analysis Workshop-19. IMPUTE2 had the advantage of using two references 1000G and a sequence for a subset of subjects. SHAPEIT2 and IMPUTE2 were used to finalise the full LLFS autosome imputation. In LLFS, 44% of ~80M autosomal imputed variants showed good imputation quality (info ≥ 0.30). Low imputation quality was associated with a predominantly low allele frequency in 1000-Genomes. New emerging large-scale sequences and enhanced imputation methodologies will further improve imputation quality.","PeriodicalId":434900,"journal":{"name":"Int. J. Bioinform. Res. Appl.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132590268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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