Briefings in bioinformatics最新文献_第8页

A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbaf030

Xikang Feng, Miaozhe Huo, He Li, Yongze Yang, Yuepeng Jiang, Liang He, Shuai Cheng Li

{"title":"A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.","authors":"Xikang Feng, Miaozhe Huo, He Li, Yongze Yang, Yuepeng Jiang, Liang He, Shuai Cheng Li","doi":"10.1093/bib/bbaf030","DOIUrl":"10.1093/bib/bbaf030","url":null,"abstract":"The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis. We assessed these models utilizing eight downstream classifiers and five downstream clustering methods, with the performance measured by a diverse range of metrics for precision, robustness, and usability. Overall, handcrafted embeddings outperformed data-driven ones in modeling TCR-epitope interactions. To further refine our comparative findings, we developed an all-in-one TCR CDR3 embedding package comprising all evaluated embedding models. This package will assist users in easily selecting suitable embedding models for their data.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

PAGE-based transfer learning from single-cell to bulk sequencing enhances model generalization for sepsis diagnosis. 基于page的从单细胞到批量测序的迁移学习增强了败血症诊断的模型泛化。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae661

Nana Jin, Chuanchuan Nan, Wanyang Li, Peijing Lin, Yu Xin, Jun Wang, Yuelong Chen, Yuanhao Wang, Kaijiang Yu, Changsong Wang, Chunbo Chen, Qingshan Geng, Lixin Cheng

{"title":"PAGE-based transfer learning from single-cell to bulk sequencing enhances model generalization for sepsis diagnosis.","authors":"Nana Jin, Chuanchuan Nan, Wanyang Li, Peijing Lin, Yu Xin, Jun Wang, Yuelong Chen, Yuanhao Wang, Kaijiang Yu, Changsong Wang, Chunbo Chen, Qingshan Geng, Lixin Cheng","doi":"10.1093/bib/bbae661","DOIUrl":"https://doi.org/10.1093/bib/bbae661","url":null,"abstract":"Sepsis, caused by infections, sparks a dangerous bodily response. The transcriptional expression patterns of host responses aid in the diagnosis of sepsis, but the challenge lies in their limited generalization capabilities. To facilitate sepsis diagnosis, we present an updated version of single-cell Pair-wise Analysis of Gene Expression (scPAGE) using transfer learning method, scPAGE2, dedicated to data fusion between single-cell and bulk transcriptome. Compared to scPAGE, the upgrade to scPAGE2 featured ameliorated Differentially Expressed Gene Pairs (DEPs) for pretraining a model in single-cell transcriptome and retrained it using bulk transcriptome data to construct a sepsis diagnostic model, which effectively transferred cell-layer information from single-cell to bulk transcriptome. Seven datasets across three transcriptome platforms and fluorescence-activated cell sorting (FACS) were used for performance validation. The model involved four DEPs, showing robust performance across next-generation sequencing and microarray platforms, surpassing state-of-the-art models with an average AUROC of 0.947 and an average AUPRC of 0.987. Analysis of scRNA-seq data reveals higher cell proportions with JAM3-PIK3AP1 expression in sepsis monocytes, decreased ARG1-CCR7 in B and T cells. Elevated IRF6-HP in sepsis monocytes confirmed by both scRNA-seq and an independent cohort using FACS. Both the superior performance of the model and the in vitro validation of IRF6-HP in monocytes emphasize that scPAGE2 is effective and robust in the construction of sepsis diagnostic model. We additionally applied scPAGE2 to acute myeloid leukemia and demonstrated its superior classification performance. Overall, we provided a strategy to improve the generalizability of classification model that can be adapted to a broad range of clinical prediction scenarios.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks. 在异性恋网络中识别癌症驱动基因的简化图神经网络研究。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae691

Xingyi Li, Jialuo Xu, Junming Li, Jia Gu, Xuequn Shang

{"title":"Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.","authors":"Xingyi Li, Jialuo Xu, Junming Li, Jia Gu, Xuequn Shang","doi":"10.1093/bib/bbae691","DOIUrl":"10.1093/bib/bbae691","url":null,"abstract":"The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convolutional neural network with representation separation and a bimodal feature extractor. The results demonstrate that SGCD not only performs exceptionally well but also exhibits robust discriminative capabilities compared to state-of-the-art methods across all benchmark datasets. Moreover, subsequent interpretability experiments on both the model and biological aspects provide compelling evidence supporting the reliability of SGCD. Additionally, the model can dissect gene modules, revealing clearer connections between driver genes in cancers. We are confident that SGCD holds potential in the field of precision oncology and may be applied to prognosticate biomarkers for a wide range of complex diseases.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142919593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

DrugAssist: a large language model for molecule optimization. DrugAssist：用于分子优化的大型语言模型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae693

Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, Xiangxiang Zeng

{"title":"DrugAssist: a large language model for molecule optimization.","authors":"Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, Xiangxiang Zeng","doi":"10.1093/bib/bbae693","DOIUrl":"10.1093/bib/bbae693","url":null,"abstract":"Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback. These non-interactive approaches overlook the fact that the drug discovery process is actually one that requires the integration of expert experience and iterative refinement. To address this gap, we propose DrugAssist, an interactive molecule optimization model which performs optimization through human-machine dialogue by leveraging LLM's strong interactivity and generalizability. DrugAssist has achieved leading results in both single and multiple property optimization, simultaneously showcasing immense potential in transferability and iterative optimization. In addition, we publicly release a large instruction-based dataset called 'MolOpt-Instructions' for fine-tuning language models on molecule optimization tasks. We have made our code and data publicly available at https://github.com/blazerye/DrugAssist, which we hope to pave the way for future research in LLMs' application for drug discovery.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations. 机器学习支持的虚拟筛选表明，通过分子对接、分子动力学模拟和生物学评价验证了阿多柔比星和夸氟辛的抗结核活性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae696

Si Zheng, Yaowen Gu, Yuzhen Gu, Yelin Zhao, Liang Li, Min Wang, Rui Jiang, Xia Yu, Ting Chen, Jiao Li

{"title":"Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.","authors":"Si Zheng, Yaowen Gu, Yuzhen Gu, Yelin Zhao, Liang Li, Min Wang, Rui Jiang, Xia Yu, Ting Chen, Jiao Li","doi":"10.1093/bib/bbae696","DOIUrl":"10.1093/bib/bbae696","url":null,"abstract":"Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb. Our screening method produced satisfactory predictions on three data-splitting settings, with the top predicted bioactive compounds all known antibacterial or anti-TB drugs. To further identify and evaluate drugs with repurposing potential in TB therapy, 15 screened potential compounds were selected for subsequent computational and experimental evaluations, out of which aldoxorubicin and quarfloxin showed potent inhibition of Mtb strain H37Rv, with minimal inhibitory concentrations of 4.16 and 20.67 μM/mL, respectively. More inspiringly, these two compounds also showed antibacterial activity against multidrug-resistant TB isolates and exhibited strong antimicrobial activity against Mtb. Furthermore, molecular docking, molecular dynamics simulation, and the surface plasmon resonance experiments validated the direct binding of the two compounds to Mtb DNA gyrase. In summary, our effective comprehensive virtual screening workflow successfully repurposed two novel drugs (aldoxorubicin and quarfloxin) as promising anti-Mtb candidates. The verification results provide useful information for the further development and clinical verification of anti-TB drugs.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11684895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The iPhylo suite: an interactive platform for building and annotating biological and chemical taxonomic trees. iPhylo套件：用于构建和注释生物和化学分类树的交互式平台。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae679

Yueer Li, Chen Peng, Fei Chi, Zinuo Huang, Mengyi Yuan, Xin Zhou, Chao Jiang

{"title":"The iPhylo suite: an interactive platform for building and annotating biological and chemical taxonomic trees.","authors":"Yueer Li, Chen Peng, Fei Chi, Zinuo Huang, Mengyi Yuan, Xin Zhou, Chao Jiang","doi":"10.1093/bib/bbae679","DOIUrl":"10.1093/bib/bbae679","url":null,"abstract":"Accurate and rapid taxonomic classifications are essential for systematically exploring organisms and metabolites in diverse environments. Many tools have been developed for biological taxonomic trees, but limitations apply, and a streamlined method for constructing chemical taxonomic trees is notably absent. We present the iPhylo suite (https://www.iphylo.net/), a comprehensive, automated, and interactive platform for biological and chemical taxonomic analysis. The iPhylo suite features web-based modules for the interactive construction and annotation of taxonomic trees and a stand-alone command-line interface (CLI) for local operation or deployment on high-performance computing (HPC) clusters. iPhylo supports National Center for Biotechnology Information (NCBI) taxonomy for biologicals and ChemOnt and NPClassifier for chemical classifications. The iPhylo visualization module, fully implemented in R, allows users to save progress locally and customize the underlying R code. Finally, the CLI module facilitates analysis across all hierarchical relational databases. We showcase the iPhylo suite's capabilities for visualizing environmental microbiomes, analyzing gut microbial metabolite synthesis preferences, and discovering novel correlations between microbiome and metabolome in humans and environment. Overall, the iPhylo suite is distinguished by its unified and interactive framework for in-depth taxonomic and integrative analyses of biological and chemical features and beyond.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11684897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

BACT: nonparametric Bayesian cell typing for single-cell spatial transcriptomics data. 单细胞空间转录组学数据的非参数贝叶斯细胞分型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae689

Yinqiao Yan, Xiangyu Luo

{"title":"BACT: nonparametric Bayesian cell typing for single-cell spatial transcriptomics data.","authors":"Yinqiao Yan, Xiangyu Luo","doi":"10.1093/bib/bbae689","DOIUrl":"10.1093/bib/bbae689","url":null,"abstract":"The spatial transcriptomics is a rapidly evolving biological technology that simultaneously measures the gene expression profiles and the spatial locations of spots. With progressive advances, current spatial transcriptomic techniques can achieve the cellular or even the subcellular resolution, making it possible to explore the fine-grained spatial pattern of cell types within one tissue section. However, most existing cell spatial clustering methods require a correct specification of the cell type number, which is hard to determine in the practical exploratory data analysis. To address this issue, we present a nonparametric Bayesian model BACT to perform BAyesian Cell Typing by utilizing gene expression information and spatial coordinates of cells. BACT incorporates a nonparametric Potts prior to induce neighboring cells' spatial dependency, and, more importantly, it can automatically learn the cell type number directly from the data without prespecification. Evaluations on three single-cell spatial transcriptomic datasets demonstrate the better performance of BACT than competing spatial cell typing methods. The R package and the user manual of BACT are publicly available at https://github.com/yinqiaoyan/BACT.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The role of automation in enhancing reproducibility and interoperability of PBPK models.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbaf053

Abdallah Derbalah, Masoud Jamei, Iain Gardner, Armin Sepp

引用次数: 0

PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data. PartIES：利用多组学数据的扩散增强相似性进行分区级整合的疾病亚型框架。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae609

Yuqi Miao, Huang Xu, Shuang Wang

{"title":"PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.","authors":"Yuqi Miao, Huang Xu, Shuang Wang","doi":"10.1093/bib/bbae609","DOIUrl":"10.1093/bib/bbae609","url":null,"abstract":"Integrating multi-omics data helps identify disease subtypes. Many similarity-based methods were developed for disease subtyping using multi-omics data, with many of them focusing on extracting common clustering structures across multiple types of omics data, but not preserving data-type-specific clustering structures. Moreover, clustering performance of similarity-based methods is affected when similarity measures are noisy. Here we proposed PartIES, a Partition-level Integration using diffusion-Enhanced Similarities to perform disease subtyping using multi-omics data. PartIES uses diffusion to reduce noises in individual similarity/kernel matrices from individual omics data types first, and then extract partition information from diffusion-enhanced similarity matrices and integrate the partition-level similarity through a weighted average iteratively. Simulation studies showed that (1) the diffusion step enhances clustering accuracy, and (2) PartIES outperforms competing methods, particularly when omics data types provide different clustering structures. Using mRNA, long noncoding RNAs, microRNAs expression data, DNA methylation data, and somatic mutation data from The Cancer Genome Atlas project, PartIES identified subtypes in bladder urothelial carcinoma, liver hepatocellular carcinoma, and thyroid carcinoma that are most significantly associated with patient survival across all methods. Further investigations suggested that among subtype-associated genes, many of those that are highly interacting with other genes are known important cancer genes. The identified cancer subtypes also have different activity levels for some known cancer-related pathways. The R code can be accessed at https://github.com/yuqimiao/PartIES.git.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11586768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics. MCGAE：通过综合多模态空间转录组学揭示肿瘤侵袭。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae608

Yiwen Yang, Chengming Zhang, Zhaonan Liu, Kazuyuki Aihara, Chuanchao Zhang, Luonan Chen, Wu Wei

{"title":"MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics.","authors":"Yiwen Yang, Chengming Zhang, Zhaonan Liu, Kazuyuki Aihara, Chuanchao Zhang, Luonan Chen, Wu Wei","doi":"10.1093/bib/bbae608","DOIUrl":"10.1093/bib/bbae608","url":null,"abstract":"Spatially Resolved Transcriptomics (SRT) serves as a cornerstone in biomedical research, revealing the heterogeneity of tissue microenvironments. Integrating multimodal data including gene expression, spatial coordinates, and morphological information poses significant challenges for accurate spatial domain identification. Herein, we present the Multi-view Contrastive Graph Autoencoder (MCGAE), a cutting-edge deep computational framework specifically designed for the intricate analysis of spatial transcriptomics (ST) data. MCGAE advances the field by creating multi-view representations from gene expression and spatial adjacency matrices. Utilizing modular modeling, contrastive graph convolutional networks, and attention mechanisms, it generates modality-specific spatial representations and integrates them into a unified embedding. This integration process is further enriched by the inclusion of morphological image features, markedly enhancing the framework's capability to process multimodal data. Applied to both simulated and real SRT datasets, MCGAE demonstrates superior performance in spatial domain detection, data denoising, trajectory inference, and 3D feature extraction, outperforming existing methods. Specifically, in colorectal cancer liver metastases, MCGAE integrates histological and gene expression data to identify tumor invasion regions and characterize cellular molecular regulation. This breakthrough extends ST analysis and offers new tools for cancer and complex disease research.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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