GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models

Shah, Syed Muazzam Ali; Taju, Semmy Wellem; Ho, Quang‐Thai; Nguyen, Trinh‐Trung‐Duong; Ou, Yu‐Yen

doi:10.1016/j.compbiomed.2021.104259

Cited by 23 publications

(5 citation statements)

References 44 publications

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“…We used the BERT model 21 , 22 to recognize language entities, entity span, semantic type of entities, and semantic relationships between language entities. BERT relies on a transformer, an attention mechanism for learning the contextual relationships between words in a text.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists

et al. 2023

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ImportanceArtificial intelligence (AI) can interpret abnormal signs in chest radiography (CXR) and generate captions, but a prospective study is needed to examine its practical value.ObjectiveTo prospectively compare natural language processing (NLP)-generated CXR captions and the diagnostic findings of radiologists.Design, Setting, and ParticipantsA multicenter diagnostic study was conducted. The training data set included CXR images and reports retrospectively collected from February 1, 2014, to February 28, 2018. The retrospective test data set included consecutive images and reports from April 1 to July 31, 2019. The prospective test data set included consecutive images and reports from May 1 to September 30, 2021.ExposuresA bidirectional encoder representation from a transformers model was used to extract language entities and relationships from unstructured CXR reports to establish 23 labels of abnormal signs to train convolutional neural networks. The participants in the prospective test group were randomly assigned to 1 of 3 different caption generation models: a normal template, NLP-generated captions, and rule-based captions based on convolutional neural networks. For each case, a resident drafted the report based on the randomly assigned captions and an experienced radiologist finalized the report blinded to the original captions. A total of 21 residents and 19 radiologists were involved.Main Outcomes and MeasuresTime to write reports based on different caption generation models.ResultsThe training data set consisted of 74 082 cases (39 254 [53.0%] women; mean [SD] age, 50.0 [17.1] years). In the retrospective (n = 8126; 4345 [53.5%] women; mean [SD] age, 47.9 [15.9] years) and prospective (n = 5091; 2416 [47.5%] women; mean [SD] age, 45.1 [15.6] years) test data sets, the mean (SD) area under the curve of abnormal signs was 0.87 (0.11) in the retrospective data set and 0.84 (0.09) in the prospective data set. The residents’ mean (SD) reporting time using the NLP-generated model was 283 (37) seconds—significantly shorter than the normal template (347 [58] seconds; P &lt; .001) and the rule-based model (296 [46] seconds; P &lt; .001). The NLP-generated captions showed the highest similarity to the final reports with a mean (SD) bilingual evaluation understudy score of 0.69 (0.24)—significantly higher than the normal template (0.37 [0.09]; P &lt; .001) and the rule-based model (0.57 [0.19]; P &lt; .001).Conclusions and RelevanceIn this diagnostic study of NLP-generated CXR captions, prior information provided by NLP was associated with greater efficiency in the reporting process, while maintaining good consistency with the findings of radiologists.

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Section: Methodsmentioning

confidence: 99%

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists

et al. 2023

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“…BERT-based models have been used in the biomedical domain ( Zhang et al, 2019 ; Sun et al, 2021 ). Furthermore, the BERT technique has been applied to protein’s amino acid sequences to generate crucial feature representations used in different downstream tasks ( Ali Shah et al, 2021 ; Ali Shah and Ou, 2021 ; Charoenkwan et al, 2021 ). Thus, we applied a BERT-based embedding model ( Vaswani et al, 2017 ; Devlin et al, 2018 ) called ProtTrans ( Elnaggar et al, 2022 ) to automatically extract crucial features from the amino-acid sequences that capture the most significant properties for each gene in our dataset.…”

Section: Methodsmentioning

confidence: 99%

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

et al. 2023

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Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

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“…Transformers' Bidirectional Encoder Representations BERT, among other pre-trained language models, significantly outperforms industry benchmarks in eleven NLP tasks, including sentence-level sentiment classification, setting a new standard for text representation [69]. To capture the semantics and context of words, BERT used the notion of contextualised word embedding [70]. BERT has demonstrated to be a straightforward yet effective language model that performed at a high level on eleven NLP tasks [71].…”

Section: Nlp Methods Based On Bert For Classificationmentioning

confidence: 99%

Semantic Context and Attention-driven Framework for Predicting Visual Description Utilizing a Deep Neural Network and Natural Language Processing

Shetty

Bhat

2023

IJCSBE

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Background/Purpose: This literature review's goal is to inspect various machine learning algorithms for visual description and their applications to prediction. Examining the numerous approaches mentioned in this area brings up a fresh avenue for expanding the current research methods. Design/Methodology/Approach: The study results that are provided in different scholarly works are acquired from secondary sources, such as scholarly journal publications. This review study analyses these articles and highlights their interpretations. Findings/Result: This research focuses on several cataloguing methods for isolated identifying images and visions. When developing research topics in the idea of inaccessible detecting geographic information systems, the gaps discovered during analysis using various methodologies have made things simpler. Research limitations/implications: This study examined a range of AI tool uses. The scope of this work is rivetted to a assessment of the many machine-learning implementation strategies for analysis and prediction. More research might be done on the many deep learning constructions for image and video classification. Originality/Value: The articles chosen for this study's review are from academic journals and are cited by other authors in their works. The articles that were selected for the examination have a connection to the investigation and research plan described in the paper. Paper Type: Literature review paper.

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GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models

Cited by 23 publications

References 44 publications

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Semantic Context and Attention-driven Framework for Predicting Visual Description Utilizing a Deep Neural Network and Natural Language Processing

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