Name
Title | ||
|---|---|---|
Collaborative Filtering Based Recommendation Algorithm For Recommending Active Molecules For Protein Targets |
Abstract | ||
|---|---|---|
Although many open databases already have provided a large amount of resources for virtual screening, they are not fully utilized in the process of drug repositioning. In the paper the recommendation algorithm in the field of E-commerce is applied to discover active molecules for drug targets. First, the dataset is extracted from the public database and the rating matrix of targets and molecules is come from the dataset; second, a userbasedCF recommendation algorithm and two improved algorithms are used for recommending the small active molecules for targets; third, through comparing two indicators, MAE and RMSE in three algorithms, the three algorithms are suitable for this field, and the third algorithm based on the reducing dimension algorithm of five layers Autoencoders has the best performance; finally, it concludes that the new idea proposed in the paper can narrow the scope of searching active molecules, improve the efficiency of drug repositioning and further accelerate the speed of drug discovery. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621560 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Collaborative filtering recommendation, Autoencoders, Dimensionality reduction, Active molecules recommendation, Drug repositioning | Drug discovery,Drug repositioning,Collaborative filtering,Dimensionality reduction,Computer science,Mean squared error,Algorithm,Artificial intelligence,Virtual screening,Machine learning | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Ma | 1 | 47 | 19.80 |
| Hongxin An | 2 | 0 | 0.34 |
| Ruisheng Zhang | 3 | 181 | 35.82 |
| Rongjing Hu | 4 | 14 | 5.24 |