Parallelization techniques for variable selection and predictive models generation and its applications
US11080606B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 16, 2017 |
| Grant date | Aug 3, 2021 |
| Priority date | — |
| Expiry date | Jan 25, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/126
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Predictive regression models are widely used in different domains such as life sciences, healthcare, pharma etc. and variable selection, is employed as one of the key steps. Variable selection can be performed using random or exhaustive search techniques. Unlike a random approach, the exhaustive search approach, evaluates each possible combination and consequently, is a computationally hard problem, thus limiting its applications. The embodiments of the present disclosure perform i) parallelization and optimization of critical time consuming steps of the technique, Variable Selection and Modeling based on the Prediction (VSMP) ii) its applications for the generation of the best possible predictive models using input dataset (e.g., Blood Brain Barrier Permeation data) and iii) business impact of predictive models that are requires the selection of larger number of variables.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.