Detection of Huntington’s Disease in Human DNA Sequence using Numerical Encoding Method and Machine Learning based Classifier
G. Tamilpavai1, C. Vishnuppriya2
1G. Tamilpavai, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.
2C. Vishnuppriya, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli,, Tamil Nadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7426-7432 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5312118419/2019©BEIESP | DOI: 10.35940/ijrte.D5312.118419

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Abstract: Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that is required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus.
Keywords: Amino Acids, Antigenicity, Normalization and Protein Modeling.
Scope of the Article: Machine Learning.