Gene expression cancer classification using modified K-Nearest Neighbors technique

Ayyad, Sarah M.; Saleh, Ahmed I.; Labib, Labib M.

doi:10.1016/j.biosystems.2018.12.009

Cited by 134 publications

(75 citation statements)

References 21 publications

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“…Finally, observed and predicted responses were compared to understand the prediction accuracy of the selected feature genes. For validation, 3 different prediction algorithms ( S upport V ector M achine: SVM [28], r andom F orest: RF and k - N earest N eighbor: k-NN [21]) were used to increase the confidence and robustness of prediction.…”

Section: Methodsmentioning

confidence: 99%

“…These down-streams effects (i.e. gene’s up or down-regulations) are easy to assess and can directly be linked to pathways or cellular processes, thereby, giving an answer to how and why a patient could be sensitive to a given therapy [21]. The advantage of such a data-driven approach is that it is free from any pre-conceptualized bias like drug targets, disease genes etc.…”

Section: Introductionmentioning

confidence: 99%

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Fast and robust method for drug response biomarker identification and sample stratification

Das¹,

Bhattacharya²

2019

Preprint

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With unprecedented progress of cancer research, the world is now prepared with versatile arsenal of drugs to combat cancer. However, response of an individual to any drug or combination treatment stands as a major challenge and hence there exists the sheer need for personalized medication. Identification of drug response biomarkers from a wholistic tumor microenvironment analysis would guide researchers to develop custom-tailored treatment regimen. In this study, a fast and robust method has been developed to identify drug response biomarkers from entire transcriptomics data analysis in a data-driven manner. The biomarkers which were identified by the method, were able to stratify patients between responders vs non-responders population. Furthermore, bayesian network (BN) analysis, done on the data, brought forth a mechanistic insight into the role of identified biomarkers in regulating drug efficacy. The importance of this work lies with the protocol that is time saving and requires less computation power, yet analyzes a whole system data and helps the researchers to take a step forward towards the development of personalized care in effective cancer treatment.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast and robust method for drug response biomarker identification and sample stratification

Das¹,

Bhattacharya²

2019

Preprint

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“…There are two objectives for the experiments carried out in this section. The first is to validate the KNNV algorithm by showing its superiority to related algorithms that can handle both heterogeneity and incompleteness in the data, namely Modified KNN (MKNN) [28], KNN for imperfect data (KNN imp ) [29] and cost-sensitive KNN (csKNN) [30]. For each algorithm, the precision, recall, accuracy, and F-Score [31] metrics were evaluated.…”

Section: Experimental Workmentioning

confidence: 99%

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Hamed

Sobhy

Nassar

2020

Preprint

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The coronavirus 2019 disease (COVID-19) is wreaking havoc around the world, and great efforts are underway to control it. Millions of people are now being tested and their data keeps accumulating in large volumes. This data can be used to classify newly tested persons as whether they have the disease or not. However, normal classification techniques are hampered by the fact that the data is typically both incomplete and heterogeneous. To address this two-fold obstacle, we propose a KNN variant (KNNV) algorithm which accurately and efficiently classifies COVID-19. The main two ideas behind the proposed algorithm are that for each instance to be classified it chooses the parameter K adaptively and calculates the distances to other instances in a novel way. The KNNV was implemented and tested on a COVID-19 dataset from the Italian society of medical and intervention radiology society. It was also compared to three algorithms of its category. The test results show that the KNNV can efficiently and accurately classify COVID-19 patients. The comparison results show that the algorithm greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score.

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“…The k-NN classifies the input data on the basis of the k-nearest neighbor's category. k-NN is used for its simplicity in many research problems, such as in [30], where it is used for the prediction of cancer using gene expression data. In our case, the dataset was continuous (integer/real), thus using k-NN was the best choice for our research, as the paper claims that the performance of k-NN on continuous datasets is better than its performance on the text data [31].…”

Section: Classifier Usedmentioning

confidence: 99%

Enhanced Feature Subset Selection Using Niche Based Bat Algorithm

2019

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Redundant and irrelevant features disturb the accuracy of the classifier. In order to avoid redundancy and irrelevancy problems, feature selection techniques are used. Finding the most relevant feature subset that can enhance the accuracy rate of the classifier is one of the most challenging parts. This paper presents a new solution to finding relevant feature subsets by the niche based bat algorithm (NBBA). It is compared with existing state of the art approaches, including evolutionary based approaches. The multi-objective bat algorithm (MOBA) selected 8, 16, and 248 features with 93.33%, 93.54%, and 78.33% accuracy on ionosphere, sonar, and Madelon datasets, respectively. The multi-objective genetic algorithm (MOGA) selected 10, 17, and 256 features with 91.28%, 88.70%, and 75.16% accuracy on same datasets, respectively. Finally, the multi-objective particle swarm optimization (MOPSO) selected 9, 21, and 312 with 89.52%, 91.93%, and 76% accuracy on the above datasets, respectively. In comparison, NBBA selected 6, 19, and 178 features with 93.33%, 95.16%, and 80.16% accuracy on the above datasets, respectively. The niche multi-objective genetic algorithm selected 8, 15, and 196 features with 93.33%, 91.93%, and 79.16 % accuracy on the above datasets, respectively. Finally, the niche multi-objective particle swarm optimization selected 9, 19, and 213 features with 91.42%, 91.93%, and 76.5% accuracy on the above datasets, respectively. Hence, results show that MOBA outperformed MOGA and MOPSO, and NBBA outperformed the niche multi-objective genetic algorithm and the niche multi-objective particle swarm optimization.

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Gene expression cancer classification using modified K-Nearest Neighbors technique

Cited by 134 publications

References 21 publications

Fast and robust method for drug response biomarker identification and sample stratification

Fast and robust method for drug response biomarker identification and sample stratification

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Enhanced Feature Subset Selection Using Niche Based Bat Algorithm

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