Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

Abstract

The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

“…Because at present the chemical and physical properties of known drug targets can be found but nondrug targets cannot, the prediction of drug targets is one classification problem for which there is no good solution. Currently, most studies usually use pending test proteins as nondrug targets, but they inevitably contain proteins that subsequently turn out to be drug targets although the majority of the proteins are normal proteins [ 28 ]. Therefore, there is an alternatively reasonable way to collect a more accurate nondrug targets dataset based on the drug targets structure characteristics.…”

“…Therefore, they can provide us with information that whether a protein is suitable to be a drug target protein. The chemical and physical properties which were used in our analysis are 26 amino acids (counted by Mole%) and number of charged residues, basic residues, acidic average molecular weight, and isoelectric point are used to train the model and predict the potential drug targets [ 28 ]. Besides, they are important clues as well as the PPI topological features for the judgement of which proteins could be targets.…”

Practical Skills of Business English Correspondence Writing Based on Data Mining Algorithm

“…However, with the in-depth research and theoretical development, especially in the 21st century, with the significant improvement of computer ability, learning natural language based on deep learning technology has gradually matured. In 2013, Wang and others proposed the word2vec algorithm and a bag-of-words model was constructed using neural network, and the word vector representation of the target language was calculated according to the context word distribution of the target language in a large-scale corpus [7]. Word2vec algorithm completes the transformation from text representation to static number vector.…”

PAIRS: Prediction of Activation/Inhibition Regulation Signaling Pathway

“…Some studies were dedicated to developing a more efficient statistical inference method. With the boom in machine learning methods and high credibility biological database [ 7 ], new methods are in great need to help identify novel pathway regulation relationships of protein interactions.…”