| Type of Material: | Thesis |
| Title: | Design and Development of Intelligent Gene Patterns Discovery Mechanisms to Predict Human Diseases |
| Researcher: | SAKTHIVEL, N K |
| Guide: | GOPALAN, N P |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2020 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2020 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSAKTHIVEL, N K|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aDesign and Development of Intelligent Gene Patterns Discovery Mechanisms to Predict Human Diseases |
| 260 | __ | |aChennai|bBharath University, Chennai|c2020 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2020 |
| 520 | __ | |aUnderstanding and predicting Human Genome Patterns are one of the challenging issues regarding human health. To achieve the highest Classification Accuracy, a large amount of Genome Data Sets need to analyze. It is noted that a single Gene is not responsible for many Human Diseases and instead, diseases occur by different or group of genomes interacting together and causes diseases. Hence it needs to analyze and associate the complete genome sequences with understanding or predicting various possible human diseases. This research work identified three recently proposed popular Genome Cluster-Classifiers, namely i. Hierarchical-Random Forest based Clustering (HRF-Cluster), ii. Genetic Algorithm-Gene Association Classifier and iii. Weighted Common Neighbor Classifier (wCN). These Classifiers were implemented and thoroughly studied in terms of Prediction Accuracy, Memory Utilization, Memory Usage and Processing Time. From our experimental results, it is noted that the performances of these three classifiers pu |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGOPALAN, N P|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/315482|yShodhganga |
| 905 | __ | |afromsg |
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