Assisting document triage for human kinome curation via machine learning

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

BMC Genomics ◽

10.1186/s12864-020-07185-7 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Jinchan Qu ◽

Albert Steppi ◽

Dongrui Zhong ◽

Jie Hao ◽

Jian Wang ◽

...

Keyword(s):

Machine Learning ◽

Language Processing ◽

Protein Interactions ◽

Learning Approach ◽

Protein Protein Interactions ◽

Final Model ◽

Protein Protein Interaction ◽

Learning Framework ◽

Machine Learning Approach ◽

Document Triage

Abstract Background Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. Results Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. Conclusions The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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