Automated knowledge extraction from polymer literature using natural language processing

Shetty, Pranav; Ramprasad, Rampi

doi:10.1016/j.isci.2020.101922

Cited by 46 publications

(35 citation statements)

References 36 publications

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“…20 Data that fuel such approaches may be efficiently and autonomously extracted from the literature using ML approaches. 21,22 In the present contribution, we direct our efforts toward building ML models that can instantaneously predict three important temperaturesthe glass transition (T g ), melting (T m ), and degradation (T d ) temperaturesof copolymers. T g and T m determine the mechanical properties of copolymers, while T d indicates the overall temperature stability of copolymers.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The burgeoning field of polymer informatics − attempts to address such critical search problems by utilizing modern data-driven machine learning (ML) approaches. − Such efforts have already seen significant successes in terms of the realization and deployment of on-demand polymer property predictors − and solving inverse problems by which polymers meeting specific property requirements are either identified from a candidate set or freshly designed using genetic or generative algorithms . Data that fuel such approaches may be efficiently and autonomously extracted from the literature using ML approaches. , …”

Section: Introductionmentioning

confidence: 99%

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Copolymer Informatics with Multitask Deep Neural Networks

2021

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Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multi-task learning and meta-learning are proposed. A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Copolymer Informatics with Multitask Deep Neural Networks

2021

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“…Their result shows that word embedding could have high potential in inorganic materials discovery. Nevertheless, the work conducted by Shetty and Ramprasad (2021) confirms that word embedding can be helpful for polymer predictions too. Rule‐based NLP techniques, such as pattern and string matching have also been discussed.…”

Section: Related Workmentioning

confidence: 97%

Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning

Zhao

Lopez

Saikin

et al. 2021

Proceedings of the Association for Information Science and Tech

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Scientific literature is one of the most significant resources for sharing knowledge. Researchers turn to scientific literature as a first step in designing an experiment. Given the extensive and growing volume of literature, the common approach of reading and manually extracting knowledge is too time consuming, creating a bottleneck in the research cycle. This challenge spans nearly every scientific domain. For the materials science, experimental data distributed across millions of publications are extremely helpful for predicting materials properties and the design of novel materials. However, only recently researchers have explored computational approaches for knowledge extraction primarily for inorganic materials. This study aims to explore knowledge extraction for organic materials. We built a research dataset composed of 855 annotated and 708,376 unannotated sentences drawn from 92,667 abstracts. We used named-entity-recognition (NER) with BiLSTM-CNN-CRF deep learning model to automatically extract key knowledge from literature. Early-phase results show a high potential for automated knowledge extraction. The paper presents our findings and a framework for supervised knowledge extraction that can be adapted to other scientific domains.

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“…However, these large-scale experimental screening techniques for the identification of ubiquitination sites are time consuming, expensive, and laborious. Owing to the advantages and emergence of machine learning models, they have been utilized in different fields, such as natural language processing (NLP) [ 28 , 29 ], energy load forecasting [ 30 ], speech recognition [ 31 ], image recognition [ 32 , 33 , 34 ], and computational biology [ 35 , 36 , 37 , 38 ]. Computational predictors were built to predict ubiquitination sites in a cost- and time-effective manner.…”

Section: Introductionmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

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Automated knowledge extraction from polymer literature using natural language processing

Cited by 46 publications

References 36 publications

Copolymer Informatics with Multitask Deep Neural Networks

Copolymer Informatics with Multitask Deep Neural Networks

Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

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