PSSM Amino-Acid Composition Based Gene Identification Using Support Vector Machines (Record no. 14253)

000 -LEADER
fixed length control field a
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20210210153818.0
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fixed length control field 210210b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 13265
Author Bhat, Heena Farooq
245 ## - TITLE STATEMENT
Title PSSM Amino-Acid Composition Based Gene Identification Using Support Vector Machines
250 ## - EDITION STATEMENT
Volume, Issue number Vol 6 (1), Jan- Apr
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Dlhi
Name of publisher, distributor, etc. STM Journals
Year 2019
300 ## - PHYSICAL DESCRIPTION
Pagination 50-58p.
520 ## - SUMMARY, ETC.
Summary, etc. main characteristic of identifying the molecular mechanism of the cell is to understand the significance or function of each protein encoded in the genome. For that purpose, genome annotation proves to be very supportive. One of the most obligatory phases of genome annotation is the prediction of the genes. Several methods or techniques have been developed in order to locate or predict the patterns of genes in genome sequence. However, still the recognition of genes is found to be a very complicated problem. Recognizing the corresponding gene of a given protein sequence by means of conventional tools is error prone. Hence, the recognition of genes is a very demanding task. In this paper, we first concentrate on the problem of gene prediction and its challenges. We then present a new method for identifying genes. This new method follows a two-step procedure. Firstly, we present new features extracted from protein sequences and these features are derived from a position specific scoring matrix (PSSM). The PSSM profiles are converted into uniform numeric representation. Finally, the PSSM vectors are given as an input to SVM for classification purpose. This new method has been demonstrated on genome DNA set dataset. It is shown that the experimental results of new approach produces better results.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 13222
Co-Author Wani, M. Arif
773 0# - HOST ITEM ENTRY
Title Journal of artificial intelligence research and advances (JoAIRA)
Place, publisher, and date of publication Noida STM Journals
856 ## - ELECTRONIC LOCATION AND ACCESS
URL http://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=1713
Link text Click Here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Articles Abstract Database
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Barcode Date last seen Price effective from Koha item type
          School of Engineering & Technology School of Engineering & Technology Archieval Section 2021-02-10 2021-2021474 2021-02-10 2021-02-10 Articles Abstract Database
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