The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator.

We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).

As suggested in recent studies, species recognition and disambiguation is one of the most critical and challenging steps in many downstream text-mining applications such as the gene normalization task and protein-protein interaction extraction. We report SR4GN: an open source tool for species recognition and disambiguation in biomedical text. In addition to the species detection function in existing tools, SR4GN is optimized for the Gene Normalization task. As such it is developed to link detected species with corresponding gene mentions in a document. SR4GN achieves 85.42% in accuracy and compares favorably to the other state-of-the-art techniques in benchmark experiments. Finally, SR4GN is implemented as a standalone software tool, thus making it convenient and robust for use in many text-mining applications. SR4GN can be downloaded at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/downloads/SR4GN

To access and utilize the rich information contained in the biomedical literature, the ability to recognize and normalize gene mentions referenced in the literature is crucial. In this paper, we focus on improvements to the accuracy of gene normalization in cases where species information is not provided. Gene names are often ambiguous, in that they can refer to the genes of many species. Therefore, gene normalization is a difficult challenge.

Objective: Month of birth (MOB) is associated with specified mental disorders (MDs). However, whether these relationships extend to all MDs remains unclear. We investigate the association using a population‐based cohort study and a meta‐analysis.Methods: First, we examined patients with 34 DSM‐5‐classified MDs in the Taiwan national database. We estimated the relative risk ratios (RR) of each illness in each MOB relative to that in the general population and assessed the periodicity, with six further sensitivity analyses. Second, we searched PubMed, Embase, and Cochrane for related articles through 31 December 2020. We used a random‐effects model, pooled RRs with 95% confidence intervals of each MOB from the identified studies, and transformed them from MOB to relative age in a year or season.Results: The cohort included 1,951,777 patients. Except for posttraumatic stress disorder, dissociative disorders, feeding/eating disorders, gender dysphoria, and paraphilic disorders, the other MDs had significant MOB periodicity. The meta‐analysis included 51 studies investigating 10 MDs. The youngest age at the start of school owing to MOB was associated with the highest RRs of intellectual disability (1.13), autism (1.05), attention‐deficit/hyperactivity disorder (1.13). Winter births had significant risks of schizophrenia (1.04), bipolar I disorder (1.02), and major depressive disorder (1.01), and autumn births had a significant risk of alcohol use disorder (1.02). No significant associations between season of birth and Alzheimer's disease, or eating disorders were found.Conclusions: MOB is related to the risks of certain MDs. This finding provides a reference for future research on the etiology of MDs.