大量原始数据存档在众多开放存取数据库中，随着新一代技术在实验室中变得越来越普遍，大型数据集将变得更加普遍。所有这些数据的归档、管理、分析和解释都是一项挑战。数据库开发和生物管理处于努力理解这种不断增加的数据洪流的最前沿。数据库：生物数据库和管理杂志提供了一个开放访问平台，用于展示数据库研究和生物管理中的新思想，旨在帮助加强数据库开发人员、管理者和用户之间的桥梁。

As next-generation technologies become increasingly common in laboratories, a vast amount of raw data is being archived in numerous open-access databases, leading to the prevalence of large datasets. Effectively archiving, managing, analyzing, and interpreting this data presents a significant challenge. Database development and biological management are at the forefront of understanding this growing data deluge. "Database: The Journal of Biological Databases and Curation" offers an open-access platform to showcase new ideas in database research and biological management, aiming to strengthen the connections between database developers, managers, and users.

随着新一代技术在实验室中的普及，越来越多的原始数据被存档在众多开放存取数据库中，导致大型数据集的出现变得愈加普遍。如何有效地归档、管理、分析和解释这些数据，成为了一项巨大的挑战。数据库开发和生物管理正处于理解这一不断增加的数据洪流的前沿。"Database: The Journal of Biological Databases and Curation" 提供了一个开放获取的平台，旨在展示数据库研究和生物管理中的新思想，帮助加强数据库开发者、管理者与用户之间的联系。

Database : the journal of biological databases and curation

Large amounts of important medical information are captured in free-text documents in biomedical research and within healthcare systems, which can be made accessible through natural language processing (NLP). A key component in most biomedical NLP pipelines is entity linking, i.e. grounding textual mentions of named entities to a reference of medical concepts, usually derived from a terminology system, such as the Systematized Nomenclature of Medicine Clinical Terms. However, complex entity mentions, spanning multiple tokens, are notoriously hard to normalize due to the difficulty of finding appropriate candidate concepts. In this work, we propose an approach to preprocess such mentions for candidate generation, building upon recent advances in text simplification with generative large language models. We evaluate the feasibility of our method in the context of the entity linking track of the BioCreative VIII SympTEMIST shared task. We find that instructing the latest Generative Pre-trained Transformer model with a few-shot prompt for text simplification results in mention spans that are easier to normalize. Thus, we can improve recall during candidate generation by 2.9 percentage points compared to our baseline system, which achieved the best score in the original shared task evaluation. Furthermore, we show that this improvement in recall can be fully translated into top-1 accuracy through careful initialization of a subsequent reranking model. Our best system achieves an accuracy of 63.6% on the SympTEMIST test set. The proposed approach has been integrated into the open-source xMEN toolkit, which is available online via https://github.com/hpi-dhc/xmen.

Florian Borchert

Ignacio Llorca

Matthieu-P Schapranow

Database

Improving biomedical entity linking for complex entity mentions with LLM-based text simplification

Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical–protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge. Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/

Yifan Peng

Anthony Rios

Ramakanth Kavuluru

Zhiyong Lu

Extracting chemical–protein relations with ensembles of SVM and deep learning models

提取化学-蛋白质关系的多支持向量机与深度学习模型集成方法

InterPro amalgamates predictive protein signatures from a number of well-known partner databases into a single resource. To aid with interpretation of results, InterPro entries are manually annotated with terms from the Gene Ontology (GO). The InterPro2GO mappings are comprised of the cross-references between these two resources and are the largest source of GO annotation predictions for proteins. Here, we describe the protocol by which InterPro curators integrate GO terms into the InterPro database. We discuss the unique challenges involved in integrating specific GO terms with entries that may describe a diverse set of proteins, and we illustrate, with examples, how InterPro hierarchies reflect GO terms of increasing specificity. We describe a revised protocol for GO mapping that enables us to assign GO terms to domains based on the function of the individual domain, rather than the function of the families in which the domain is found. We also discuss how taxonomic constraints are dealt with and those cases where we are unable to add any appropriate GO terms. Expert manual annotation of InterPro entries with GO terms enables users to infer function, process or subcellular information for uncharacterized sequences based on sequence matches to predictive models. Database URL: http://www.ebi.ac.uk/interpro. The complete InterPro2GO mappings are available at: ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/external2go/interpro2go

S. Burge

E. Kelly

D. Lonsdale

P. Mutowo-Muellenet

C. McAnulla

A. Mitchell

A. Sangrador-Vegas

S.-Y. Yong

N. Mulder

S. Hunter

Manual GO annotation of predictive protein signatures: the InterPro approach to GO curation

预测蛋白质特征的手动GO注释：InterPro在GO注释中的方法

Rice [ Oryza sativa (L.)] feeds more than half of the world's population. Rhizoctonia solani is a major fungal pathogen of rice causing extreme crop losses in all rice-growing regions of the world. R. solani AG1 IA is a major cause of sheath blight in rice. In this study, we constructed a comprehensive and user-friendly web-based database, RSIADB, to analyse its draft genome and transcriptome. The database was built using the genome sequence (10 489 genes) and annotation information for R. solani AG1 IA. A total of six RNAseq samples of R. solani AG1 IA were also analysed, corresponding to 10, 18, 24, 32, 48 and 72 h after infection of rice leaves.

Lei Chen

Peng Ai

Jinfeng Zhang

Qiming Deng

Shiquan Wang

Shuangcheng Li

Jun Zhu

Ping Li

Aiping Zheng

RSIADB, a collective resource for genome and transcriptome analyses in<i>Rhizoctonia solani</i>AG1 IA

RSIADB：一种用于<i>Rhizoctonia solani</i> AG1 IA基因组和转录组分析的集体资源

The human oral microbiome is the most studied human microflora, but 53% of the species have not yet been validly named and 35% remain uncultivated. The uncultivated taxa are known primarily from 16S rRNA sequence information. Sequence information tied solely to obscure isolate or clone numbers, and usually lacking accurate phylogenetic placement, is a major impediment to working with human oral microbiome data. The goal of creating the Human Oral Microbiome Database (HOMD) is to provide the scientific community with a body site-specific comprehensive database for the more than 600 prokaryote species that are present in the human oral cavity based on a curated 16S rRNA gene-based provisional naming scheme. Currently, two primary types of information are provided in HOMD—taxonomic and genomic. Named oral species and taxa identified from 16S rRNA gene sequence analysis of oral isolates and cloning studies were placed into defined 16S rRNA phylotypes and each given unique Human Oral Taxon (HOT) number. The HOT interlinks phenotypic, phylogenetic, genomic, clinical and bibliographic information for each taxon. A BLAST search tool is provided to match user 16S rRNA gene sequences to a curated, full length, 16S rRNA gene reference data set. For genomic analysis, HOMD provides comprehensive set of analysis tools and maintains frequently updated annotations for all the human oral microbial genomes that have been sequenced and publicly released. Oral bacterial genome sequences, determined as part of the Human Microbiome Project, are being added to the HOMD as they become available. We provide HOMD as a conceptual model for the presentation of microbiome data for other human body sites. Database URL: http://www.homd.org

T. Chen

W.-H. Yu

J. Izard

O. V. Baranova

A. Lakshmanan

F. E. Dewhirst

The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information

人类口腔微生物组数据库：用于研究口腔微生物分类和基因组信息的网络资源

Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. However, it did not acquire enough attention. Less information, statistics, or data on the disease has been revealed. Thus, no systematic concept datasets were released to help clinicians clarify the scope, assist research, and offer maximized value. This study aimed to develop a cross-lingual atherosclerotic cerebrovascular disease ontology; describe the workflow, schema, hierarchical structure, and the highlighted content; design a brand-new rehabilitation ontology; implement the ontology evaluation; and illustrate the application scenarios in real-world scenarios. We implemented nine steps based on the Ontology Development 101 methodologies combined with expert opinions. The ontology included collection and specification of clinical requirements, background investigation and knowledge acquisition, ontology selection and reuse, scope identification, schema definition, concept extraction, concept extension, ontology verification, and ontology evaluation. We evaluated the proposed ontology in the literature classification task. The current ontology included 10 top-level classes, respectively, clinical manifestation, comorbidity, complication, diagnosis, model of atherosclerotic cerebrovascular disease, pathogenesis, prevention, rehabilitation, risk factor, and treatment. There are 1715 concepts in the 11-level ontology, covering 4588 Chinese terms, 6617 English terms, and 972 definitions. The ontology could be applied in real-world scenarios such as information retrieval, new expression discovery, named entity recognition, and knowledge fusion, and the use case proved that it could offer satisfying support to related medical scenarios. The ontology was proven to be useful in text classification tasks, and the weight-F1 score could reach >80% combined with the pretrained model. The proposed ontology provided a clear set of cross-lingual concepts and terms with an explicit hierarchical structure, helping scientific researchers to quickly retrieve relevant medical literature, assisting data scientists to efficiently identify relevant contents in electronic health records, and providing a clear domain framework for academic reference. Database URL: https://bioportal.bioontology.org/ontologies/ACVD_ONTOLOGY.

Hetong Ma

Liu Shen

Jiayang Wang

Shilong Wang

Min Wang

Meng Wang

Zixiao Li

Jiao Li

Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology

迈向更清晰的识别与更便捷的应用：跨语言动脉粥样硬化性脑血管疾病本体的发展

Abstract Non-coding RNAs (ncRNAs) are traditionally considered incapable of encoding proteins, but new evidence suggests that small open reading frames (sORFs) within ncRNAs can actually encode biologically functional small peptides. Despite growing recognition of their importance, a systematic exploration of plant ncRNAs with coding potential has remained largely uncharted territory, especially in the context of their translational activities. By collecting and analyzing Ribo-Seq data from 226 Arabidopsis thaliana samples, we have integrated extensive information on Arabidopsis ncRNAs with coding potential and developed the AthRiboNC database, a novel and dedicated database that consolidates extensive information on ncRNAs with coding potential in Arabidopsis. AthRiboNC covers detailed information on 2743 long non-coding RNAs, 255 microRNAs, and 1871 circular RNA in Arabidopsis, along with 40 162 ORFs identified from these ncRNAs. The database also constructs co-expression networks for ncRNAs with coding potential, revealing correlations and potential biological function interpretations. With a commitment to accessibility and ease-of-use, AthRiboNC features a clear and intuitive interface. We hope that AthRiboNC will serve as a valuable resource for exploring the coding potential of plant ncRNAs. Database URL: https://bis.zju.edu.cn/athribonc

Yi Shen

Liya Liu

Enyan Liu

Sida Li

Yuriy Orlov

Vladimir Ivanisenko

Ming Chen

AthRiboNC: an Arabidopsis database for ncRNAs with coding potential revealed from ribosome profiling

AthRiboNC：一个基于核糖体图谱揭示的拟南芥编码潜力非编码RNA数据库

Abstract Biomedical Relation Extraction (RE) is central to Biomedical Natural Language Processing and is crucial for various downstream applications. Existing RE challenges in the field of biology have primarily focused on intra-sentential analysis. However, with the rapid increase in the volume of literature and the complexity of relationships between biomedical entities, it often becomes necessary to consider multiple sentences to fully extract the relationship between a pair of entities. Current methods often fail to fully capture the complex semantic structures of information in documents, thereby affecting extraction accuracy. Therefore, unlike traditional RE methods that rely on sentence-level analysis and heuristic rules, our method focuses on extracting entity relationships from biomedical literature titles and abstracts and classifying relations that are novel findings. In our method, a multitask training approach is employed for fine-tuning a Pre-trained Language Model in the field of biology. Based on a broad spectrum of carefully designed tasks, our multitask method not only extracts relations of better quality due to more effective supervision but also achieves a more accurate classification of whether the entity pairs are novel findings. Moreover, by applying a model ensemble method, we further enhance our model’s performance. The extensive experiments demonstrate that our method achieves significant performance improvements, i.e. surpassing the existing baseline by 3.94% in RE and 3.27% in Triplet Novel Typing in F1 score on BioRED, confirming its effectiveness in handling complex biomedical literature RE tasks. Database URL: https://codalab.lisn.upsaclay.fr/competitions/13377#learn_the_details-dataset

Jiru Li

Dinghao Pan

Zhihao Yang

Yuanyuan Sun

Hongfei Lin

Jian Wang

JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps

JTIS：通过联合训练中间步骤增强生物医学文档级关系抽取

Abstract There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types—such as disease names, cell types, or chemicals—that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze, or integrate with other datasets due to the upfront curation effort required to make the data usable—typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata—either in bulk or in a one-by-one fashion, e.g. to support autocompletion of biomedical entities in forms—we have developed an open-source tool called text2term that maps free-text descriptions of biomedical entities to controlled terms in ontologies. The tool is highly configurable and can be used in multiple ways that cater to different users and expertise levels—it is available on Python Package Index and can be used programmatically as any Python package; it can also be used via a command-line interface or via our hosted, graphical user interface–based web application or by deploying a local instance of our interactive application using Docker. Database URL: https://pypi.org/project/text2term

Rafael S Gonçalves

Jason Payne

Amelia Tan

Carmen Benitez

Jamie Haddock

Robert Gentleman

The text2term tool to map free-text descriptions of biomedical terms to ontologies

text2term工具将生物医学术语的自由文本描述映射到本体

Abbreviation	Database. (Oxford).
Journal Impact	3.38
Quartiles(Global)	MATHEMATICAL & COMPUTATIONAL BIOLOGY(Q1)

ISSN	1758-0463
h-index	82

Publication Information	Publisher: Oxford University Press，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2009，Proportion of original research papers: 99.11%，Self Citation Rate:0.00%， Gold OA Rate: 93.81%
Journal Fees	$2598Details
Average review cycle	网友分享经验：>12周，或约稿

Database : the journal of biological databases and curation

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Improving biomedical entity linking for complex entity mentions with LLM-based text simplification

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Manual GO annotation of predictive protein signatures: the InterPro approach to GO curation

RSIADB, a collective resource for genome and transcriptome analyses inRhizoctonia solaniAG1 IA

The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information

Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology

AthRiboNC: an Arabidopsis database for ncRNAs with coding potential revealed from ribosome profiling

JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps

Improving biomedical entity linking for complex entity mentions with LLM-based text simplification

The text2term tool to map free-text descriptions of biomedical terms to ontologies