Random Forest classification based on star graph topological indices for antioxidant proteins

Abstract

Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard, there exists a tiny subset of molecules in nature, named antioxidant proteins that may influence the aging process. However, testing every single protein in order to identify its properties is quite expensive and inefficient. For this reason, this work proposes a model, in which the primary structure of the protein is represented using complex network graphs that can be used to reduce the number of proteins to be tested for antioxidant biological activity. The graph obtained as a representation will help us describe the complex system by using topological indices. More specifically, in this work, Randić's Star Networks have been used as well as the associated indices, calculated with the S2SNet tool. In order to simulate the existing proportion of antioxidant proteins in nature, a dataset containing 1999 proteins, of which 324 are antioxidant proteins, was created. Using this data as input, Star Graph Topological Indices were calculated with the S2SNet tool. These indices were then used as input to several classification techniques. Among the techniques utilised, the Random Forest has shown the best performance, achieving a score of 94% correctly classified instances. Although the target class (antioxidant proteins) represents a tiny subset inside the dataset, the proposed model is able to achieve a percentage of 81.8% correctly classified instances for this class, with a precision of 81.3%.

Identification of Antioxidants from Sequence Information Using Naïve Bayes

“…Recently, Fernandez-Blanco et al reported a computational model to identify antioxidant proteins based on star graph topological indices [2]. However, by analyzing Fernandez-Blanco et al's dataset, we found that sequences in their dataset share high-sequence similarities; some sequences in their dataset even share 100% sequences identity.…”

“…Fernandez-Blanco et al have constructed a dataset containing 324 proteins with antioxidant activity and 1657 proteins without [2]. However, sequences in their dataset share high-sequence identity.…”

“…After following the aforementioned strict screening procedures, we obtained 686 proteins with antioxidant activity and obtained a new raw dataset by merging the 686 proteins into Fernandez-Blanco et al's dataset [2]. …”

Adopting Multivariate Nonparametric Tools to Determine Genotype-Phenotype Interactions in Health and Disease

“…RF models showed their utility not only in highlighting functional regions but also in scoring protein interactions from one organism model into another to be used in protein interaction networks [51]. The antioxidant biological activity of proteins associated to star graph topographical indices was better highlighted with RF, when compared with other methods [52].…”

Prehospital triage of trauma patients using the Random Forest computer algorithm

“…10 The Random Forest Model (RFM) is an ensemble classifier that uses a combination of many decision trees. The decision trees are created using a labeled training set of data associated with each patient.…”