Briefings in Functional Genomics最新文献_第10页

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data. 利用单细胞RNA测序数据，机器学习在胶质母细胞瘤内异质性研究中的应用。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-10 DOI: 10.1093/bfgp/elad002

Harold Brayan Arteaga-Arteaga, Mariana S Candamil-Cortés, Brian Breaux, Pablo Guillen-Rondon, Simon Orozco-Arias, Reinel Tabares-Soto

{"title":"Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data.","authors":"Harold Brayan Arteaga-Arteaga, Mariana S Candamil-Cortés, Brian Breaux, Pablo Guillen-Rondon, Simon Orozco-Arias, Reinel Tabares-Soto","doi":"10.1093/bfgp/elad002","DOIUrl":"10.1093/bfgp/elad002","url":null,"abstract":"Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03% pm 5.37%$, $97.42% pm 3.94%$, $98.27% pm 1.81%$ and $93.04% pm 6.88%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9518963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing Klf4 mRNA. 更正：STAT3依赖性长非编码RNA Lncenc1通过稳定Klf4 mRNA促进小鼠ES细胞的多能性。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-10-18 DOI: 10.1093/bfgp/elad047

引用次数: 0

STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing K mRNA. STAT3依赖的长非编码RNA Lncenc1通过稳定KmRNA促进小鼠ES细胞的多能性。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-10-04 DOI: 10.1093/bfgp/elad045

Emanuele Monteleone, Paola Corrieri, Paolo Provero, Daniele Viavattene, Lorenzo Pulvirenti, Laura Raggi, Elena Carbognin, Marco E Bianchi, Graziano Martello, Salvatore Oliviero, Pier Paolo Pandolfi, Valeria Poli

{"title":"STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing K mRNA.","authors":"Emanuele Monteleone, Paola Corrieri, Paolo Provero, Daniele Viavattene, Lorenzo Pulvirenti, Laura Raggi, Elena Carbognin, Marco E Bianchi, Graziano Martello, Salvatore Oliviero, Pier Paolo Pandolfi, Valeria Poli","doi":"10.1093/bfgp/elad045","DOIUrl":"10.1093/bfgp/elad045","url":null,"abstract":"Embryonic stem cells (ESCs) preserve the unique ability to differentiate into any somatic cell lineage while maintaining their self-renewal potential, relying on a complex interplay of extracellular signals regulating the expression/activity of pluripotency transcription factors and their targets. Leukemia inhibitory factor (LIF)-activated STAT3 drives ESCs' stemness by a number of mechanisms, including the transcriptional induction of pluripotency factors such as Klf4 and the maintenance of a stem-like epigenetic landscape. However, it is unknown if STAT3 directly controls stem-cell specific non-coding RNAs, crucial to balance pluripotency and differentiation. Applying a bioinformatic pipeline, here we identify Lncenc1 in mouse ESCs as an STAT3-dependent long non-coding RNA that supports pluripotency. Lncenc1 acts in the cytoplasm as a positive feedback regulator of the LIF-STAT3 axis by competing for the binding of microRNA-128 to the 3'UTR of the Klf4 core pluripotency factor mRNA, enhancing its expression. Our results unveil a novel non-coding RNA-based mechanism for LIF-STAT3-mediated pluripotency.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41160189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data. 基于多精度数据的逐步深度学习解释SNP组合对精神分裂症病因的影响。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-09-21 DOI: 10.1093/bfgp/elad041

Yousang Jo, Maree J Webster, Sanghyeon Kim, Doheon Lee

{"title":"Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.","authors":"Yousang Jo, Maree J Webster, Sanghyeon Kim, Doheon Lee","doi":"10.1093/bfgp/elad041","DOIUrl":"https://doi.org/10.1093/bfgp/elad041","url":null,"abstract":"Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41123956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. 更正为基于 Omics 的深度学习方法用于肺癌决策和疗法开发。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-09-14 DOI: 10.1093/bfgp/elad046

引用次数: 0

SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data. SCMcluster:一种整合标记基因集和单细胞RNA测序数据的高精度细胞聚类算法。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-07-17 DOI: 10.1093/bfgp/elad004

Hao Wu, Haoru Zhou, Bing Zhou, Meili Wang

{"title":"SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data.","authors":"Hao Wu, Haoru Zhou, Bing Zhou, Meili Wang","doi":"10.1093/bfgp/elad004","DOIUrl":"https://doi.org/10.1093/bfgp/elad004","url":null,"abstract":"Single-cell clustering is the most significant part of single-cell RNA sequencing (scRNA-seq) data analysis. One main issue facing the scRNA-seq data is noise and sparsity, which poses a great challenge for the advance of high-precision clustering algorithms. This study adopts cellular markers to identify differences between cells, which contributes to feature extraction of single cells. In this work, we propose a high-precision single-cell clustering algorithm-SCMcluster (single-cell cluster using marker genes). This algorithm integrates two cell marker databases(CellMarker database and PanglaoDB database) with scRNA-seq data for feature extraction and constructs an ensemble clustering model based on the consensus matrix. We test the efficiency of this algorithm and compare it with other eight popular clustering algorithms on two scRNA-seq datasets derived from human and mouse tissues, respectively. The experimental results show that SCMcluster outperforms the existing methods in both feature extraction and clustering performance. The source code of SCMcluster is available for free at https://github.com/HaoWuLab-Bioinformatics/SCMcluster.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9833773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Single-cell multi-omics sequencing and its application in tumor heterogeneity. 单细胞多组学测序及其在肿瘤异质性研究中的应用。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-07-17 DOI: 10.1093/bfgp/elad009

Yuqing Sun, Zhiyu Liu, Yue Fu, Yuwei Yang, Junru Lu, Min Pan, Tian Wen, Xueying Xie, Yunfei Bai, Qinyu Ge

引用次数: 0

Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers. 利用失调的动态网络生物标志物检测流感预警信号。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-07-17 DOI: 10.1093/bfgp/elad006

Yanhao Huo, Chuchu Li, Yujie Li, Xianbin Li, Peng Xu, Zhenshen Bao, Wenbin Liu

引用次数: 0

MiRNA-gene network embedding for predicting cancer driver genes. mirna -基因网络嵌入预测癌症驱动基因。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-07-17 DOI: 10.1093/bfgp/elac059

Wei Peng, Rong Wu, Wei Dai, Yu Ning, Xiaodong Fu, Li Liu, Lijun Liu

{"title":"MiRNA-gene network embedding for predicting cancer driver genes.","authors":"Wei Peng, Rong Wu, Wei Dai, Yu Ning, Xiaodong Fu, Li Liu, Lijun Liu","doi":"10.1093/bfgp/elac059","DOIUrl":"https://doi.org/10.1093/bfgp/elac059","url":null,"abstract":"The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene-gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene-miRNA network. First, we constructed a gene-miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9833012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1

An integrated complete-genome sequencing and systems biology approach to predict antimicrobial resistance genes in the virulent bacterial strains of Moraxella catarrhalis. 综合全基因组测序和系统生物学方法预测卡他莫拉菌毒力菌株的抗微生物药物耐药基因。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-07-17 DOI: 10.1093/bfgp/elad005

Sadia Afrin Bristy, Md Arju Hossain, Md Imran Hasan, S M Hasan Mahmud, Mohammad Ali Moni, Md Habibur Rahman

{"title":"An integrated complete-genome sequencing and systems biology approach to predict antimicrobial resistance genes in the virulent bacterial strains of Moraxella catarrhalis.","authors":"Sadia Afrin Bristy, Md Arju Hossain, Md Imran Hasan, S M Hasan Mahmud, Mohammad Ali Moni, Md Habibur Rahman","doi":"10.1093/bfgp/elad005","DOIUrl":"https://doi.org/10.1093/bfgp/elad005","url":null,"abstract":"Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising 74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks. According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic target replacement, alteration and antibiotic efflux pump processes. They exhibit resistance to several antibiotics, such as isoniazid, ethionamide, cycloserine, fosfomycin, triclosan, etc. Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant interactors in the interaction network and are therefore regarded as the hub nodes. These genes can be exploited to create novel medications by serving as possible therapeutic targets. Finally, we believe that our findings could be useful to advance knowledge of the AMR system present in M. catarrhalis.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9833781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1