Comprehensively identifying Long Covid articles with human-in-the-loop machine learning

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid collection shows that (1) most Long Covid articles do not refer to Long Covid by any name (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovi

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development

MyGeneFriends : towards a new relationship between researchers and big data

Ces dernières années, la biologie a subi une profonde mutation, impulsée notamment par les technologies à haut débit et la montée de la génomique personnalisée. L’augmentation massive et constante de l’information biologique qui en résulte offre de nouvelles opportunités pour comprendre la fonction et l’évolution des gènes et génomes à différentes échelles et leurs rôles dans les maladies humaines. Ma thèse s’est articulée autour de la relation entre chercheurs et information biologique, et j’ai contribué à (OrthoInspector) ou créé (Parsec, MyGeneFriends) des systèmes permettant aux chercheurs d’accéder, analyser, visualiser, filtrer et annoter en temps réel l’énorme quantité de données disponibles à l’ère post génomique. MyGeneFriends est un premier pas dans une direction passionnante, faire en sorte que ce ne soient plus les chercheurs qui aillent vers l’information, mais que l’information pertinente aille vers les chercheurs sous une forme adaptée, permettant l’accès personnalisé et efficace aux grandes quantités d’informations, la visualisation deces informations et leur interconnexion en réseaux.In recent years, biology has undergone a profound evolution, mainly due to high through put technologies and the rise of personal genomics. The resulting constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and genomes at different scales and their roles in human diseases. My thesis addressed the relationship between researchers and biological information, and I contributed to (OrthoInspector) or created (Parsec, MyGeneFriends) systems allowing researchers to access, analyze, visualize, filter and annotate in real time the enormous quantity of data available in the post genomic era. MyGeneFriends is a first step in an exciting new direction: where researchers no longer search forinformation, but instead pertinent information is brought to researchers in a suitable form, allowing personalized and efficient access to large amounts of information, visualization of this information,and their integration in networks

MyGeneFriends : towards a new relationship between researchers and big data

Ces dernières années, la biologie a subi une profonde mutation, impulsée notamment par les technologies à haut débit et la montée de la génomique personnalisée. L’augmentation massive et constante de l’information biologique qui en résulte offre de nouvelles opportunités pour comprendre la fonction et l’évolution des gènes et génomes à différentes échelles et leurs rôles dans les maladies humaines. Ma thèse s’est articulée autour de la relation entre chercheurs et information biologique, et j’ai contribué à (OrthoInspector) ou créé (Parsec, MyGeneFriends) des systèmes permettant aux chercheurs d’accéder, analyser, visualiser, filtrer et annoter en temps réel l’énorme quantité de données disponibles à l’ère post génomique. MyGeneFriends est un premier pas dans une direction passionnante, faire en sorte que ce ne soient plus les chercheurs qui aillent vers l’information, mais que l’information pertinente aille vers les chercheurs sous une forme adaptée, permettant l’accès personnalisé et efficace aux grandes quantités d’informations, la visualisation deces informations et leur interconnexion en réseaux.In recent years, biology has undergone a profound evolution, mainly due to high through put technologies and the rise of personal genomics. The resulting constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and genomes at different scales and their roles in human diseases. My thesis addressed the relationship between researchers and biological information, and I contributed to (OrthoInspector) or created (Parsec, MyGeneFriends) systems allowing researchers to access, analyze, visualize, filter and annotate in real time the enormous quantity of data available in the post genomic era. MyGeneFriends is a first step in an exciting new direction: where researchers no longer search forinformation, but instead pertinent information is brought to researchers in a suitable form, allowing personalized and efficient access to large amounts of information, visualization of this information,and their integration in networks

MyGeneFriends : vers un nouveau rapport entre chercheurs et mégadonnées

In recent years, biology has undergone a profound evolution, mainly due to high through put technologies and the rise of personal genomics. The resulting constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and genomes at different scales and their roles in human diseases. My thesis addressed the relationship between researchers and biological information, and I contributed to (OrthoInspector) or created (Parsec, MyGeneFriends) systems allowing researchers to access, analyze, visualize, filter and annotate in real time the enormous quantity of data available in the post genomic era. MyGeneFriends is a first step in an exciting new direction: where researchers no longer search forinformation, but instead pertinent information is brought to researchers in a suitable form, allowing personalized and efficient access to large amounts of information, visualization of this information,and their integration in networks.Ces dernières années, la biologie a subi une profonde mutation, impulsée notamment par les technologies à haut débit et la montée de la génomique personnalisée. L’augmentation massive et constante de l’information biologique qui en résulte offre de nouvelles opportunités pour comprendre la fonction et l’évolution des gènes et génomes à différentes échelles et leurs rôles dans les maladies humaines. Ma thèse s’est articulée autour de la relation entre chercheurs et information biologique, et j’ai contribué à (OrthoInspector) ou créé (Parsec, MyGeneFriends) des systèmes permettant aux chercheurs d’accéder, analyser, visualiser, filtrer et annoter en temps réel l’énorme quantité de données disponibles à l’ère post génomique. MyGeneFriends est un premier pas dans une direction passionnante, faire en sorte que ce ne soient plus les chercheurs qui aillent vers l’information, mais que l’information pertinente aille vers les chercheurs sous une forme adaptée, permettant l’accès personnalisé et efficace aux grandes quantités d’informations, la visualisation deces informations et leur interconnexion en réseaux

Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.

Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips-and a web tool, PubReCheck-guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors' work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck