Database: The Journal of Biological Databases and Curation最新文献_第9页

LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations. LSD600：第一个带有生活方式与疾病关系注释的生物医学摘要语料库。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-13 DOI: 10.1093/database/baae129

Esmaeil Nourani, Evangelia-Mantelena Makri, Xiqing Mao, Sampo Pyysalo, Søren Brunak, Katerina Nastou, Lars Juhl Jensen

{"title":"LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations.","authors":"Esmaeil Nourani, Evangelia-Mantelena Makri, Xiqing Mao, Sampo Pyysalo, Søren Brunak, Katerina Nastou, Lars Juhl Jensen","doi":"10.1093/database/baae129","DOIUrl":"https://doi.org/10.1093/database/baae129","url":null,"abstract":"Lifestyle factors (LSFs) are increasingly recognized as instrumental in both the development and control of diseases. Despite their importance, there is a lack of methods to extract relations between LSFs and diseases from the literature, a step necessary to consolidate the currently available knowledge into a structured form. As simple co-occurrence-based relation extraction (RE) approaches are unable to distinguish between the different types of LSF-disease relations, context-aware models such as transformers are required to extract and classify these relations into specific relation types. However, no comprehensive LSF-disease RE system existed, nor a corpus suitable for developing one. We present LSD600 (available at https://zenodo.org/records/13952449), the first corpus specifically designed for LSF-disease RE, comprising 600 abstracts with 1900 relations of eight distinct types between 5027 diseases and 6930 LSF entities. We evaluated LSD600's quality by training a RoBERTa model on the corpus, achieving an F-score of 68.5% for the multilabel RE task on the held-out test set. We further validated LSD600 by using the trained model on the two Nutrition-Disease and FoodDisease datasets, where it achieved F-scores of 70.7% and 80.7%, respectively. Building on these performance results, LSD600 and the RE system trained on it can be valuable resources to fill the existing gap in this area and pave the way for downstream applications. Database URL: https://zenodo.org/records/13952449.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations. LSD600：第一个带有生活方式与疾病关系注释的生物医学摘要语料库。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-13 DOI: 10.1093/database/baae129

Esmaeil Nourani, Evangelia-Mantelena Makri, Xiqing Mao, Sampo Pyysalo, Søren Brunak, Katerina Nastou, Lars Juhl Jensen

{"title":"LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations.","authors":"Esmaeil Nourani, Evangelia-Mantelena Makri, Xiqing Mao, Sampo Pyysalo, Søren Brunak, Katerina Nastou, Lars Juhl Jensen","doi":"10.1093/database/baae129","DOIUrl":"10.1093/database/baae129","url":null,"abstract":"Lifestyle factors (LSFs) are increasingly recognized as instrumental in both the development and control of diseases. Despite their importance, there is a lack of methods to extract relations between LSFs and diseases from the literature, a step necessary to consolidate the currently available knowledge into a structured form. As simple co-occurrence-based relation extraction (RE) approaches are unable to distinguish between the different types of LSF-disease relations, context-aware models such as transformers are required to extract and classify these relations into specific relation types. However, no comprehensive LSF-disease RE system existed, nor a corpus suitable for developing one. We present LSD600 (available at https://zenodo.org/records/13952449), the first corpus specifically designed for LSF-disease RE, comprising 600 abstracts with 1900 relations of eight distinct types between 5027 diseases and 6930 LSF entities. We evaluated LSD600's quality by training a RoBERTa model on the corpus, achieving an F-score of 68.5% for the multilabel RE task on the held-out test set. We further validated LSD600 by using the trained model on the two Nutrition-Disease and FoodDisease datasets, where it achieved F-scores of 70.7% and 80.7%, respectively. Building on these performance results, LSD600 and the RE system trained on it can be valuable resources to fill the existing gap in this area and pave the way for downstream applications. Database URL: https://zenodo.org/records/13952449.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: The landscape of microRNA interaction annotation: analysis of three rare disorders as a case study. 修正：microRNA相互作用的景观注释：作为案例研究的三种罕见疾病的分析。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-13 DOI: 10.1093/database/baae131

引用次数: 0

HoloFood Data Portal: holo-omic datasets for analysing host-microbiota interactions in animal production. 全息食品数据门户：用于分析动物生产中宿主-微生物群相互作用的全息数据集。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-11 DOI: 10.1093/database/baae112

Alexander B Rogers, Varsha Kale, Germana Baldi, Antton Alberdi, M Thomas P Gilbert, Dipayan Gupta, Morten T Limborg, Sen Li, Thomas Payne, Bent Petersen, Jacob A Rasmussen, Lorna Richardson, Robert D Finn

{"title":"HoloFood Data Portal: holo-omic datasets for analysing host-microbiota interactions in animal production.","authors":"Alexander B Rogers, Varsha Kale, Germana Baldi, Antton Alberdi, M Thomas P Gilbert, Dipayan Gupta, Morten T Limborg, Sen Li, Thomas Payne, Bent Petersen, Jacob A Rasmussen, Lorna Richardson, Robert D Finn","doi":"10.1093/database/baae112","DOIUrl":"https://doi.org/10.1093/database/baae112","url":null,"abstract":"The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues. The HoloFood Data Portal is a web resource that simplifies access to the project datasets. For example, users can conveniently access multiomic datasets derived from the same individual or retrieve host phenotypic data with a linked gut microbiome sample. Project-specific metagenome-assembled genome and viral catalogues are also provided, linking to broader datasets in MGnify. The portal stores only data necessary to provide these relationships, with possible linking to the underlying repositories. The portal showcases a model approach for how future multiomics datasets can be made available. Database URL: https://www.holofooddata.org.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DisGeNet: a disease-centric interaction database among diseases and various associated genes. DisGeNet：疾病和各种相关基因之间以疾病为中心的相互作用数据库。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-11 DOI: 10.1093/database/baae122

Yaxuan Hu, Xingli Guo, Yao Yun, Liang Lu, Xiaotai Huang, Songwei Jia

{"title":"DisGeNet: a disease-centric interaction database among diseases and various associated genes.","authors":"Yaxuan Hu, Xingli Guo, Yao Yun, Liang Lu, Xiaotai Huang, Songwei Jia","doi":"10.1093/database/baae122","DOIUrl":"10.1093/database/baae122","url":null,"abstract":"The pathogenesis of complex diseases is intricately linked to various genes and network medicine has enhanced understanding of diseases. However, most network-based approaches ignore interactions mediated by noncoding RNAs (ncRNAs) and most databases only focus on the association between genes and diseases. Based on the mentioned questions, we have developed DisGeNet, a database focuses not only on the disease-associated genes but also on the interactions among genes. Here, the associations between diseases and various genes, as well as the interactions among these genes are integrated into a disease-centric network. As a result, there are a total of 502 688 interactions/associations involving 6697 diseases, 5780 lncRNAs (long noncoding RNAs), 16 135 protein-coding genes, and 2610 microRNAs stored in DisGeNet. These interactions/associations can be categorized as protein-protein, lncRNA-disease, microRNA-gene, microRNA-disease, gene-disease, and microRNA-lncRNA. Furthermore, as users input name/ID of diseases/genes for search, the interactions/associations about the search content can be browsed as a list or viewed in a local network-view. Database URL: https://disgenet.cn/.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HoloFood Data Portal: holo-omic datasets for analysing host-microbiota interactions in animal production. 全息食品数据门户：用于分析动物生产中宿主-微生物群相互作用的全息数据集。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-11 DOI: 10.1093/database/baae112

Alexander B Rogers, Varsha Kale, Germana Baldi, Antton Alberdi, M Thomas P Gilbert, Dipayan Gupta, Morten T Limborg, Sen Li, Thomas Payne, Bent Petersen, Jacob A Rasmussen, Lorna Richardson, Robert D Finn

{"title":"HoloFood Data Portal: holo-omic datasets for analysing host-microbiota interactions in animal production.","authors":"Alexander B Rogers, Varsha Kale, Germana Baldi, Antton Alberdi, M Thomas P Gilbert, Dipayan Gupta, Morten T Limborg, Sen Li, Thomas Payne, Bent Petersen, Jacob A Rasmussen, Lorna Richardson, Robert D Finn","doi":"10.1093/database/baae112","DOIUrl":"10.1093/database/baae112","url":null,"abstract":"The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues. The HoloFood Data Portal is a web resource that simplifies access to the project datasets. For example, users can conveniently access multiomic datasets derived from the same individual or retrieve host phenotypic data with a linked gut microbiome sample. Project-specific metagenome-assembled genome and viral catalogues are also provided, linking to broader datasets in MGnify. The portal stores only data necessary to provide these relationships, with possible linking to the underlying repositories. The portal showcases a model approach for how future multiomics datasets can be made available. Database URL: https://www.holofooddata.org.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DisGeNet: a disease-centric interaction database among diseases and various associated genes. DisGeNet：疾病和各种相关基因之间以疾病为中心的相互作用数据库。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2025-01-11 DOI: 10.1093/database/baae122

Yaxuan Hu, Xingli Guo, Yao Yun, Liang Lu, Xiaotai Huang, Songwei Jia

{"title":"DisGeNet: a disease-centric interaction database among diseases and various associated genes.","authors":"Yaxuan Hu, Xingli Guo, Yao Yun, Liang Lu, Xiaotai Huang, Songwei Jia","doi":"10.1093/database/baae122","DOIUrl":"https://doi.org/10.1093/database/baae122","url":null,"abstract":"The pathogenesis of complex diseases is intricately linked to various genes and network medicine has enhanced understanding of diseases. However, most network-based approaches ignore interactions mediated by noncoding RNAs (ncRNAs) and most databases only focus on the association between genes and diseases. Based on the mentioned questions, we have developed DisGeNet, a database focuses not only on the disease-associated genes but also on the interactions among genes. Here, the associations between diseases and various genes, as well as the interactions among these genes are integrated into a disease-centric network. As a result, there are a total of 502 688 interactions/associations involving 6697 diseases, 5780 lncRNAs (long noncoding RNAs), 16 135 protein-coding genes, and 2610 microRNAs stored in DisGeNet. These interactions/associations can be categorized as protein-protein, lncRNA-disease, microRNA-gene, microRNA-disease, gene-disease, and microRNA-lncRNA. Furthermore, as users input name/ID of diseases/genes for search, the interactions/associations about the search content can be browsed as a list or viewed in a local network-view. Database URL: https://disgenet.cn/.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GeniePool 2.0: advancing variant analysis through CHM13-T2T, AlphaMissense, gnomAD V4 integration, and variant co-occurrence queries. GeniePool 2.0：通过CHM13-T2T、AlphaMissense、gnomAD V4集成和变体共现查询推进变体分析。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2024-12-27 DOI: 10.1093/database/baae130

Grisha Weintraub, Noam Hadar, Ehud Gudes, Shlomi Dolev, Ohad S Birk

{"title":"GeniePool 2.0: advancing variant analysis through CHM13-T2T, AlphaMissense, gnomAD V4 integration, and variant co-occurrence queries.","authors":"Grisha Weintraub, Noam Hadar, Ehud Gudes, Shlomi Dolev, Ohad S Birk","doi":"10.1093/database/baae130","DOIUrl":"10.1093/database/baae130","url":null,"abstract":"Originally developed to meet the challenges of genomic data deluge, GeniePool emerged as a pioneering platform, enabling efficient storage, accessibility, and analysis of vast genomic datasets, enabled due to its data lake architecture. Building on this foundation, GeniePool 2.0 advances genomic analysis through the integration of cutting-edge variant databases, such as CHM13-T2T, AlphaMissense, and gnomAD V4, coupled with the capability for variant co-occurrence queries. This evolution offers an unprecedented level of granularity and scope in genomic analyses, from enhancing our understanding of variant pathogenicity and phenotypic associations to facilitating research collaborations. The introduction of CHM13-T2T provides a more accurate reference for human genetic variation, AlphaMissense enriches the platform with protein-level impact predictions of missense mutations, and gnomAD V4 offers a comprehensive view of human genetic diversity. Additionally, the innovative feature for variant co-occurrence analysis is pivotal for exploring the combined effects of genetic variations, advancing our comprehension of compound heterozygosity, epistasis, and polygenic risk factors in disease pathogenesis. GeniePool 2.0 is a comprehensive and scalable platform, which aims to enhance genomic data analysis and contribute to genomic research, potentially supporting new discoveries and clinical innovations. Database URL: https://GeniePool.link.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11673193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images. AneRBC数据集：使用红细胞图像进行计算机辅助贫血诊断的基准数据集。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2024-12-25 DOI: 10.1093/database/baae120

Muhammad Shahzad, Syed Hamad Shirazi, Muhammad Yaqoob, Zakir Khan, Assad Rasheed, Israr Ahmed Sheikh, Asad Hayat, Huiyu Zhou

{"title":"AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.","authors":"Muhammad Shahzad, Syed Hamad Shirazi, Muhammad Yaqoob, Zakir Khan, Assad Rasheed, Israr Ahmed Sheikh, Asad Hayat, Huiyu Zhou","doi":"10.1093/database/baae120","DOIUrl":"10.1093/database/baae120","url":null,"abstract":"Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We introduce a benchmark RBC image dataset named Anemic RBC (AneRBC) to overcome this problem. This dataset is divided into two versions: AneRBC-I and AneRBC-II. AneRBC-I contains 1000 microscopic images, including 500 healthy and 500 anaemic images with 1224 × 960 pixel resolution, along with manually generated ground truth of each image. Each image contains approximately 1550 RBC elements, including normocytes, microcytes, macrocytes, elliptocytes, and target cells, resulting in a total of approximately 1 550 000 RBC elements. The dataset also includes each image's complete blood count and morphology reports to validate the CNN model results with clinical data. Under the supervision of a team of expert pathologists, the annotation, labeling, and ground truth for each image were generated. Due to the high resolution, each image was divided into 12 subimages with ground truth and incorporated into AneRBC-II. AneRBC-II comprises a total of 12 000 images, comprising 6000 original and 6000 anaemic RBC images. Four state-of-the-art CNN models were applied for segmentation and classification to validate the proposed dataset. Database URL: https://data.mendeley.com/preview/hms3sjzt7f?a=4d0ba42a-cc6f-4777-adc4-2552e80db22b.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11918253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MiCK: a database of gut microbial genes linked with chemoresistance in cancer patients. MiCK：与癌症患者化疗耐药相关的肠道微生物基因数据库。

IF 3.4 4区生物学

Database: The Journal of Biological Databases and Curation Pub Date : 2024-12-21 DOI: 10.1093/database/baae124

Muhammad Shahzaib, Muhammad Muaz, Muhammad Hasnain Zubair, Masood Ur Rehman Kayani

{"title":"MiCK: a database of gut microbial genes linked with chemoresistance in cancer patients.","authors":"Muhammad Shahzaib, Muhammad Muaz, Muhammad Hasnain Zubair, Masood Ur Rehman Kayani","doi":"10.1093/database/baae124","DOIUrl":"10.1093/database/baae124","url":null,"abstract":"Cancer remains a global health challenge, with significant morbidity and mortality rates. In 2020, cancer caused nearly 10 million deaths, making it the second leading cause of death worldwide. The emergence of chemoresistance has become a major hurdle in successfully treating cancer patients. Recently, human gut microbes have been recognized for their role in modulating drug efficacy through their metabolites, ultimately leading to chemoresistance. The currently available databases are limited to knowledge regarding the interactions between gut microbiome and drugs. However, a database containing the human gut microbial gene sequences, and their effect on the efficacy of chemotherapy for cancer patients has not yet been developed. To address this challenge, we present the Microbial Chemoresistance Knowledgebase (MiCK), a comprehensive database that catalogs microbial gene sequences associated with chemoresistance. MiCK contains 1.6 million sequences of 29 gene types linked to chemoresistance and drug metabolism, curated manually from recent literature and sequence databases. The database can support downstream analysis as it provides a user-friendly web interface for sequence search and download functionalities. MiCK aims to facilitate the understanding and mitigation of chemoresistance in cancers by serving as a valuable resource for researchers. Database URL: https://microbialchemreskb.com/.","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0