Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies

Paullada, Amandalynne; Percha, Bethany; Cohen, Trevor

doi:10.18653/v1/2020.bionlp-1.4

Cited by 6 publications

(7 citation statements)

References 36 publications

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“…Vector cosine similarity in an unsupervised word embedding enabled the prediction of applications for materials years before their publication in the materials science literature ( 49 ). Several supervised analogy learning methods based on word embeddings have been successfully applied in a variety of natural language processing tasks ( 22 , 50 , 51 ). Our algorithm uses this approach to leverage information about cancer and kinases latent in the published literature.…”

Section: Discussionmentioning

confidence: 99%

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Ravanmehr

Blau

Cappelletti

et al. 2021

NAR Genomics and Bioinformatics

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Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

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Section: Discussionmentioning

confidence: 99%

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Ravanmehr

Blau

Cappelletti

et al. 2021

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Vector cosine similarity was taken recently to enable prediction of materials for applications years before their publication in the materials science literature by unsupervised word embedding (39). Several supervised analogy learning methods based on word embeddings have been successfully applied in a variety of natural language processing tasks (13,40,41). Our algorithm uses this approach to leverage information about cancer and kinases latent in the published literature.…”

Section: Discussionmentioning

confidence: 99%

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Ravanmehr

Blau

Cappelletti

et al. 2021

Preprint

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Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of more than 530 PKs have been targeted by FDA-approved medications and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on co-occurrence patterns in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. In historical data, associations between PKs and specific cancers could be predicted years in advance with good accuracy. Our model may be a tool to predict the relevance of inhibiting PKs with specific cancers.

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“…These analogies are also known to be solvable by addition and subtraction of the "neural" word embeddings of the corresponding concepts [274,318]. Following a similar approach, the authors in [316] proposed to improve the results by training shallow neural networks using a dependency path of relations between terms in sentences.…”

Section: Holistic Transformationsmentioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [322, 327] is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [223] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

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Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies

Cited by 6 publications

References 36 publications

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

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