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The proximity of ideas: An analysis of patent text using machine learning.

ABSTRACT: This paper introduces a measure of the proximity in ideas using unsupervised machine learning. Knowledge transfers are considered a key driving force of innovation and regional economic growth. I explore knowledge relationships by deriving vector space representations of a patent's abstract text using Document Vectors (Doc2Vec), and using cosine similarity to measure their proximity in ideas space. I illustrate the potential uses of this method with an application to geographic localization in knowledge spillovers. For patents in the same technology field, their normalized text similarity is 0.02-0.05 S.D.s higher if they are located within the same city, compared to patents from other cities. This effect is much smaller than when knowledge transfers are measured using normalized patent citations: local patents receive about 0.23-0.30 S.D.s more local citations than compared to non-local control patents. These findings suggest that the effect of geography on knowledge transfers may be much smaller than the previous literature using citations suggests.

SUBMITTER: Feng S

PROVIDER: S-EPMC7347140 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The proximity of ideas: An analysis of patent text using machine learning.

Feng Sijie S

PloS one 20200709 7

This paper introduces a measure of the proximity in ideas using unsupervised machine learning. Knowledge transfers are considered a key driving force of innovation and regional economic growth. I explore knowledge relationships by deriving vector space representations of a patent's abstract text using Document Vectors (Doc2Vec), and using cosine similarity to measure their proximity in ideas space. I illustrate the potential uses of this method with an application to geographic localization in k ...[more]

PMID: 32645050

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Project description:The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

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Dataset Information

The proximity of ideas: An analysis of patent text using machine learning.

Publications

The proximity of ideas: An analysis of patent text using machine learning.

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