Improving average ranking precision in user searches for biomedical research datasets

Did high frequency phone surveys during the COVID-19 pandemic include disability questions? An assessment of COVID-19 surveys from March 2020 to December 2022

OBJECTIVES

In the midst of the pandemic, face-to-face data collection for national censuses and surveys was suspended due to limitations on mobility and social distancing, limiting the collection of already scarce disability data. Responses to these constraints were met with a surge of high-frequency phone surveys (HFPSs) that aimed to provide timely data for understanding the socioeconomic impacts of and responses to the pandemic. This paper provides an assessment of HFPS datasets and their inclusion of disability questions to evaluate the visibility of persons with disabilities during the COVID-19 pandemic.

DESIGN

We collected HFPS questionnaires conducted globally from the onset of the pandemic emergency in March 2020 until December 2022 from various online survey repositories. Each HFPS questionnaire was searched using a set of keywords for inclusion of different types of disability questions. Results were recorded in an Excel review log, which was manually reviewed by two researchers.

METHODS

The review of HFPS datasets involved two stages: (1) a main review of 294 HFPS dataset-waves and (2) a semiautomated review of the same dataset-waves using a search engine-powered questionnaire review tool developed by our team. The results from the main review were compared with those of a sensitivity analysis using and testing the tool as an alternative to manual search.

RESULTS

Roughly half of HFPS datasets reviewed and 60% of the countries included in this study had some type of question on disability. While disability questions were not widely absent from HFPS datasets, only 3% of HFPS datasets included functional difficulty questions that meet international standards. The search engine-powered questionnaire review tool proved to be able to streamline the search process for future research on inclusive data.

CONCLUSIONS

The dearth of functional difficulty questions and the Washington-Group Short Set in particular in HFPS has contributed to the relative invisibility of persons with disabilities during the pandemic emergency, the lingering effects of which could impede policy-making, monitoring and advocacy on behalf of persons with disabilities.

Online Health Search Via Multidimensional Information Quality Assessment Based on Deep Language Models: Algorithm Development and Validation

BACKGROUND

Widespread misinformation in web resources can lead to serious implications for individuals seeking health advice. Despite that, information retrieval models are often focused only on the query-document relevance dimension to rank results.

OBJECTIVE

We investigate a multidimensional information quality retrieval model based on deep learning to enhance the effectiveness of online health care information search results.

METHODS

In this study, we simulated online health information search scenarios with a topic set of 32 different health-related inquiries and a corpus containing 1 billion web documents from the April 2019 snapshot of Common Crawl. Using state-of-the-art pretrained language models, we assessed the quality of the retrieved documents according to their usefulness, supportiveness, and credibility dimensions for a given search query on 6030 human-annotated, query-document pairs. We evaluated this approach using transfer learning and more specific domain adaptation techniques.

RESULTS

In the transfer learning setting, the usefulness model provided the largest distinction between help- and harm-compatible documents, with a difference of +5.6%, leading to a majority of helpful documents in the top 10 retrieved. The supportiveness model achieved the best harm compatibility (+2.4%), while the combination of usefulness, supportiveness, and credibility models achieved the largest distinction between help- and harm-compatibility on helpful topics (+16.9%). In the domain adaptation setting, the linear combination of different models showed robust performance, with help-harm compatibility above +4.4% for all dimensions and going as high as +6.8%.

CONCLUSIONS

These results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval. Thus, our approach could be used to enhance searches made by individuals seeking online health information.

Systematic tissue annotations of genomics samples by modeling unstructured metadata

There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for genomics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the genomics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto .

Biodiversity Community Integrated Knowledge Library (BiCIKL)

BiCIKL is an European Union Horizon 2020 project that will initiate and build a new European starting community of key research infrastructures, establishing open science practices in the domain of biodiversity through provision of access to data, associated tools and services at each separate stage of and along the entire research cycle. BiCIKL will provide new methods and workflows for an integrated access to harvesting, liberating, linking, accessing and re-using of subarticle-level data (specimens, material citations, samples, sequences, taxonomic names, taxonomic treatments, figures, tables) extracted from literature. BiCIKL will provide for the first time access and tools for seamless linking and usage tracking of data along the line: specimens > sequences > species > analytics > publications > biodiversity knowledge graph > re-use.

Information retrieval in an infodemic: the case of COVID-19 publications

Background

The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19–related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.

Objective

In the context of searching for scientific evidence in the deluge of COVID-19–related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.

Methods

Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.

Results

The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25–based baseline, retrieving on average, 83% of relevant documents in the top 20.

Conclusions

These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19–related questions posed using natural language.

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development

INTRODUCTION

The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks.

METHODS

In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time.

RESULTS

Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention.

DISCUSSION

The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

UPCLASS: a deep learning-based classifier for UniProtKB entry publications

In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliographies in UniProt, we investigate a convolutional neural network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge of categorizing publications at the accession annotation level is that the same publication can be annotated with multiple proteins and thus be associated with different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a micro F1-score of 0.72 and a macro F1-score of 0.62, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such an approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession. Database URL: https://goldorak.hesge.ch/bioexpclass/upclass/.

Baseline and extensions approach to information retrieval of complex medical data: Poznan's approach to the bioCADDIE 2016

Database URL

https://biocaddie.org/benchmark-data.

Accelerating annotation of articles via automated approaches: evaluation of the neXtA5 curation-support tool by neXtProt

The development of efficient text-mining tools promises to boost the curation workflow by significantly reducing the time needed to process the literature into biological databases. We have developed a curation support tool, neXtA5, that provides a search engine coupled with an annotation system directly integrated into a biocuration workflow. neXtA5 assists curation with modules optimized for the thevarious curation tasks: document triage, entity recognition and information extraction.Here, we describe the evaluation of neXtA5 by expert curators. We first assessed the annotations of two independent curators to provide a baseline for comparison. To evaluate the performance of neXtA5, we submitted requests and compared the neXtA5 results with the manual curation. The analysis focuses on the usability of neXtA5 to support the curation of two types of data: biological processes (BPs) and diseases (Ds). We evaluated the relevance of the papers proposed as well as the recall and precision of the suggested annotations.The evaluation of document triage by neXtA5 precision showed that both curators agree with neXtA5 for 67 (BP) and 63% (D) of abstracts, while curators agree on accepting or rejecting an abstract ~80% of the time. Hence, the precision of the triage system is satisfactory.For concept extraction, curators approved 35 (BP) and 25% (D) of the neXtA5 annotations. Conversely, neXtA5 successfully annotated up to 36 (BP) and 68% (D) of the terms identified by curators. The user feedback obtained in these tests highlighted the need for improvement in the ranking function of neXtA5 annotations. Therefore, we transformed the information extraction component into an annotation ranking system. This improvement results in a top precision (precision at first rank) of 59 (D) and 63% (BP). These results suggest that when considering only the first extracted entity, the current system achieves a precision comparable with expert biocurators.