Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.
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http://dx.doi.org/10.1038/s41467-023-36476-2 | DOI Listing |
J Vasc Interv Radiol
January 2025
Associate Professor of Radiology and Imaging Sciences, Division of Interventional Radiology and Image-Guided Medicine, Emory University School of Medicine, Atlanta, GA.
This study assesses the feasibility of Large Language Models like GPT-4 (OpenAI, San Francisco, CA, USA) to summarize interventional radiology (IR) procedural reports to improve layperson understanding and translate medical texts into multiple languages. 200 reports from eight categories were summarized using GPT-4. Readability was assessed with Flesch-Kincaid Reading Level (FKRL) and Flesch Reading Ease Score (FRES).
View Article and Find Full Text PDFArch Clin Neuropsychol
January 2025
Departments of Neurology and Medicine, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
There are no published guidelines regarding remotely training research assistants (RAs) to conduct neuropsychological tests. With technological advances allowing for increased international collaboration within the medical and research communities, challenges often arise from such partnerships, including linguistic, cultural, and physical barriers. A notable challenge for supervising neuropsychologists in international projects is the physical distance from RAs, sites, and materials, making training/supervision of RAs and monitoring test data quite challenging.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Department of Chinese Language and Literature, College of Humanities, Southwest Jiaotong University, No. 999, Xi'an Road, Pidu District, Chengdu, Sichuan Province, 611756, The People's Republic of China.
The degree of semantic equivalence of translation pairs is typically measured by asking bilinguals to rate the semantic similarity of them or comparing the number and meaning of dictionary entries. Such measures are subjective, labor-intensive, and unable to capture the fine-grained variation in the degree of semantic equivalence. Thompson et al.
View Article and Find Full Text PDFJ Autism Dev Disord
January 2025
The First Hospital of Jinan University, Guangzhou, China.
Purpose: Children with autism spectrum disorder (ASD) often show abnormal speech prosody. Tonal languages can pose more difficulties as speakers need to use acoustic cues to make lexical contrasts while encoding the focal function, but the acquisition of speech prosody of non-native languages, especially tonal languages has rarely been investigated.
Methods: This study aims to fill in the aforementioned gap by studying prosodic focus-marking in Mandarin by native Cantonese-speaking children with ASD (n = 25), in comparison with their typically developing (TD) peers (n = 20) and native Mandarin-speaking children (n = 20).
JAMIA Open
February 2025
Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam 14482, Germany.
Objective: To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.
Materials And Methods: We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation.
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