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Computational prediction of genomic instability in cancer development / (Record no. 84080)

MARC details
000 -LEADER
fixed length control field 03147cam a2200349 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GiCUC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250223032915.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220201s2021 ua dh f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GiCUC
Language of cataloging eng
Transcribing agency EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library Deposite
097 ## - Thesis Degree
Thesis Level Ph.D
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number Cai01.20.03.Ph.D.2021.Mo.C
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mohamed Elsayed Ghoneimy
245 10 - TITLE STATEMENT
Title Computational prediction of genomic instability in cancer development /
Statement of responsibility, etc. Mohamed Elsayed Ghoneimy ; Supervised Hesham Ahmed Hassan , Amr Badr , Sherif Elkhamisy
246 15 - VARYING FORM OF TITLE
Title proper/short title التنبؤ الحوسبى لعدم الثبات الج{u٠٦أأ}نى فى تطور السرطان
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cairo :
Name of publisher, distributor, etc. Mohamed Elsayed Ghoneimy ,
Date of publication, distribution, etc. 2021
300 ## - PHYSICAL DESCRIPTION
Extent 87 Leaves :
Other physical details charts , facsimiles ;
Dimensions 30cm
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Ph.D.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science
520 ## - SUMMARY, ETC.
Summary, etc. Cancer is one of the most common life-threatening diseases. There is remarkably rapid development in producing cancer genomic datasets, especially the microarray datasets. Despite the abundance of data in the microarray datasets, they have a drawback, which is dimensionality. In addition to the multi-omic datasets, which combine more than one biological dataset for the same patients set, each is related to a specific type of biological features. The insights we gain for a particular biological problem or disease are double using multi-omic rather than using one dataset. It is like investigating a problem from many dimensions rather than using one dimension. On the other hand, the difficulty of analysis is increased. Therefore, new special mathematical models are needed to deal with these new cancer datasets. In this thesis, we propose two methods to deal with these new cancer datasets. At first, we propose a new filter-based gene selection method that merges the Dragonfly algorithm and the correlation-based feature selection to reduce the redundancy between the genes selected and increase the relevance between the selected genes and the decision. The proposed method is compared with nine famous feature selection methods.The experiments are applied to five widely used public microarray datasets. The used evaluation criterion of the selected features is the average accuracy of classification using three different classifiers: support vector machine, naïve Bayes, and decision tree. Experimental results demonstrate that the proposed method is efficient and performs better than the other nine methods used in the experiment. It also shows that the proposed method can be used with any one of the three classifiers included in our study to obtain an efficient automatic cancer diagnostic system
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Cancer development
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Computational prediction
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Genomic instability
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Amr Badr,
Relator term
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Hesham Ahmed Hassan ,
Relator term
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Sherif Elkhamisy ,
Relator term
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://172.23.153.220/th.pdf">http://172.23.153.220/th.pdf</a>
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Nazla
Reviser Revisor
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Shimaa
Reviser Cataloger
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Holdings
Source of classification or shelving scheme Not for loan Home library Current library Date acquired Full call number Barcode Date last seen Koha item type Copy number
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 11.02.2024 Cai01.20.03.Ph.D.2021.Mo.C 01010110085323000 22.09.2023 Thesis  
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة مخـــزن الرســائل الجـــامعية - البدروم 11.02.2024 Cai01.20.03.Ph.D.2021.Mo.C 01020110085323000 22.09.2023 CD - Rom 85323.CD