Although the published models differed in terms of the method, number of descriptors, and number of compounds (in the training set), the accuracy can be calculated to compare the performance of all models.
There are two methods for construction of descriptor vector F for chemical protein interaction from the protein descriptor vector F t (F t (i), i = 1, 2,…, pt) and chemical descriptor vector F d (F d (i), i = 1, 2,…, pd): 1.
The method applies the technique of descriptor systems by considering sensor faults as an auxiliary state variable.
Our goal is to show examples of nested cross-validation results and its benefits, and not to analyse why one method or set of descriptors performed better than the other.
The comparison result between proposed STMS and the baseline descriptors is given in Table 1, where "Combine 1" is the method of combining three baseline descriptors, "Combine 2" is the combination of the four descriptors (i.e., HOG, HOF, MBH, and STMS).
The traditional method consists of selecting a set of descriptor genes (gene signatures) using a variety of statistical methods [ 1- 5].
I highlight the subject of descriptor modelling methods for rational catalyst design, and summarise some simple validation approaches that can help us separate knowledge from garbage.
Based on H∞ theory in descriptor systems, a method of designing output-feedback dynamical compensators for uncertain descriptor systems with sensor and actuator failures is presented.
Early during the development of the method, scoring the similarity of descriptors with Pearson's r and Spearman's rank order correlation coefficient [52] was tried.
Qualitative and quantitative models were developed using different learning methods and different sets of descriptors, implemented in OCHEM.
