Patent application EP17176197A (EP), filed 2017-06-15
Publication number: EP3258388A1
Title: PARALLELIZATION TECHNIQUES FOR VARIABLE SELECTION AND PREDICTIVE MODELS GENERATION AND ITS APPLICATIONS
Abstract: [0001]    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.
IPC codes: G06F  17/    18            A I
Claims: number: 1
text: A processor implemented method (200), comprising:
number: 2
text: The processor implemented method of claim 1, wherein when R<a> is less thanRint , the one or more predictive models are generated for each of the subset.
number: 3
text: The processor implemented method of claim 1, further comprising calculating model correlation coefficientRm based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables.
number: 4
text: The processor implemented method of claim 3, further comprising:
number: 5
text: The processor implemented method of claim 1, further comprising:
number: 6
text: The processor implemented method of claim 1, wherein the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets.
number: 7
text: A system (100) comprising:
number: 8
text: The system of claim 7, wherein whenRa is less thanRint , the one or more predictive models are generated for each of the subset.
number: 9
text: The system of claim 7, wherein the one or more hardware processors (104) are further configured by the instructions to calculate model correlation coefficientRm based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables.
number: 10
text: The system of claim 9, wherein the one or more hardware processors (104) are further configured by the instructions to:
number: 11
text: The system of claim 7, wherein the one or more hardware processors (104) are further configured by the instructions to:
number: 12
text: The system of claim 7, wherein the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets.
number: 13
text: One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes:
number: 14
text: The one or more non-transitory machine readable information storage mediums of claim 13, wherein whenRa is less thanRint , the one or more predictive models are generated for each of the subset, and wherein the one or more instructions which when executed by the one or more hardware processors further cause:
number: 15
text: The one or more non-transitory machine readable information storage mediums of claim 13, wherein the one or more instructions which when executed by the one or more hardware processors further cause:
Office action: search_report, dated 2017-12-20
Prior art cited: ref_id: - LAURO C. M. DE PAULA ET AL: "A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems", PLOS ONE, vol. 9, no. 12, 10 December 2014 (2014-12-10), pages e114145, XP055417188, DOI: 10.1371/journal.pone.0114145
ref_type: npl
severity: background
ref_id: - ALBERTO GUILLÉN ET AL: "Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator", PARALLEL ARCHITECTURES AND BIOINSPIRED ALGORITHMS, 1 January 2012 (2012-01-01), pages 243 - 266, XP055417359, Retrieved from the Internet <URL:https://www.researchgate.net/profile/Amaury_Lendasse/publication/264273595_Evolutive_Approaches_for_Variable_Selection_Using_a_Non-parametric_Noise_Estimator/links/553da1360cf29b5ee4bcdccc/Evolutive-Approaches-for-Variable-Selection-Using-a-Non-parametric-Noise-Estimator.pdf> [retrieved on 20171019], DOI: 10.1007/978-3-642-28789-3-11
ref_type: npl
severity: background
ref_id: - SHU-SHEN LIU ET AL: "VSMP: A Novel Variable Selection and Modeling Method Based on the Prediction", JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES., vol. 43, no. 3, 4 April 2003 (2003-04-04), US, pages 964 - 969, XP055417367, ISSN: 0095-2338, DOI: 10.1021/ci020377j
ref_type: npl
severity: background
ref_id: - LAURO C. M. DE PAULA ET AL, "A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems", PLOS ONE, (20141210), vol. 9, no. 12, doi:10.1371/journal.pone.0114145, page e114145, XP055417188 [I] 1-15 * abstract * * page  1  - pages  3, 8-12 *
ref_type: npl
severity: background
ref_id: - Alberto Guillén ET AL, "Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator", Parallel architectures and bioinspired algorithms, doi:10.1007/978-3-642-28789-3-11, (20120101), pages 243 - 266, URL: https://www.researchgate.net/profile/Amaury_Lendasse/publication/264273595_Evolutive_Approaches_for_Variable_Selection_Using_a_Non-parametric_Noise_Estimator/links/553da1360cf29b5ee4bcdccc/Evolutive-Approaches-for-Variable-Selection-Using-a-Non-parametric-Noise-Estimator.pdf, (20171019), XP055417359 [I] 1-15 * abstract * * section 2, 5 *
ref_type: npl
severity: background
ref_id: - SHU-SHEN LIU ET AL, "VSMP: A Novel Variable Selection and Modeling Method Based on the Prediction", JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES., US, (20030404), vol. 43, no. 3, doi:10.1021/ci020377j, ISSN 0095-2338, pages 964 - 969, XP055417367 [A] 1-15 * abstract; figure 1 *
ref_type: npl
severity: background
Final disposition: pending (2017-12-20)