ADAPTIVE SPARSE K-MEANS AND OPTIMIZATION ENABLED NEURAL NETWORK FROM GENE-EXPRESSION DATA FOR CANCER CLASSIFICATION.
Cancer is one of the malignant diseases existing globally and the people affected with cancer are rescued only when the disease is recognized at the earliest possible stage. Identify in advance of disease is essential as in the final stage; since the chance of living/existence is partial. The indica...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2993 - 3005 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006321&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006321 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006321 151006321 151006321 151006321 ppf: 2993 ppct: 12 formats: fmt: @attributes: type: P tig: atl: ADAPTIVE SPARSE K-MEANS AND OPTIMIZATION ENABLED NEURAL NETWORK FROM GENE-EXPRESSION DATA FOR CANCER CLASSIFICATION. aug: au: BURRA, LAKSHMI RAMANI RAVI, BHRAMARAMBA RAMJI, BANOTHU ALLADA, APPARNA TUMULURU, PRAVEEN affil: Assistant Professor, Dept. of CSE, PVP Siddhartha Institute of Technology, Vijayawada sug: subj: Neural Networks (Computer) Gene Expression Neoplasms Classification Health Beliefs Algorithms Utilization Microarray Analysis Databases Sequence Analysis ab: Cancer is one of the malignant diseases existing globally and the people affected with cancer are rescued only when the disease is recognized at the earliest possible stage. Identify in advance of disease is essential as in the final stage; since the chance of living/existence is partial. The indications of cancers are difficult and thus, all the indications should be considered accurately earlier to the diagnosis. Thus, an automatic prediction system is essential for classifying the tumor to malignant or benign. This work introduces a cancer classification approach using Chronological Grasshopper Optimization Algorithm (Chronological-GOA) for classification of cancer. For reducing the dimension of gene-expression data, log transformation is applied to the database. Then, the adaptive sparse K-means clustering selects the necessary data, which is provided to the Deep Belief Network (DBN). Here, the DBN is trained using Chronological-GOA. At last, the DBN classifies the selected gene sequences as normal and abnormal gene, and thereby identify the cancer. The performance of the cancer classification based on MSparse Kmeans + Chronological GOA-DBN is computed based on accuracy, detection rate, and False Alarm Rate (FAR). The developed method attains the accuracy of 0.9876, maximal detection rate of 0.9893, and the minimal FAR of 0.0596. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|