Open AccessA hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions
Author(s) -
Bipin Nair B.J,
Lijo Joy
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.9.9752
Subject(s) - hot spot (computer programming) , classifier (uml) , artificial intelligence , computer science , curse of dimensionality , deep learning , feature extraction , machine learning , pattern recognition (psychology) , drug target , representation (politics) , data mining , medicine , pharmacology , law , politics , political science , operating system
In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.