Correction to: Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics
Brief Bioinform. 2024 Mar 27;25(3):bbae155. doi: 10.1093/bib/bbae155.NO ABSTRACTPMID:38581651 | PMC:PMC10998650 | DOI:10.1093/bib/bbae155 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Source Type: research
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model
Brief Bioinform. 2024 Mar 27;25(3):bbae133. doi: 10.1093/bib/bbae133.ABSTRACTPeptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this fi...
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Le Zhang Wenkai Song Tinghao Zhu Yang Liu Wei Chen Yang Cao Source Type: research
Integrated modeling of protein and RNA
Brief Bioinform. 2024 Mar 27;25(3):bbae139. doi: 10.1093/bib/bbae139.NO ABSTRACTPMID:38561980 | PMC:PMC10985284 | DOI:10.1093/bib/bbae139 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Haoquan Liu Yunjie Zhao Source Type: research
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model
Brief Bioinform. 2024 Mar 27;25(3):bbae133. doi: 10.1093/bib/bbae133.ABSTRACTPeptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this fi...
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Le Zhang Wenkai Song Tinghao Zhu Yang Liu Wei Chen Yang Cao Source Type: research
Integrated modeling of protein and RNA
Brief Bioinform. 2024 Mar 27;25(3):bbae139. doi: 10.1093/bib/bbae139.NO ABSTRACTPMID:38561980 | PMC:PMC10985284 | DOI:10.1093/bib/bbae139 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Haoquan Liu Yunjie Zhao Source Type: research
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model
Brief Bioinform. 2024 Mar 27;25(3):bbae133. doi: 10.1093/bib/bbae133.ABSTRACTPeptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this fi...
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Le Zhang Wenkai Song Tinghao Zhu Yang Liu Wei Chen Yang Cao Source Type: research
Integrated modeling of protein and RNA
Brief Bioinform. 2024 Mar 27;25(3):bbae139. doi: 10.1093/bib/bbae139.NO ABSTRACTPMID:38561980 | PMC:PMC10985284 | DOI:10.1093/bib/bbae139 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Haoquan Liu Yunjie Zhao Source Type: research
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model
Brief Bioinform. 2024 Mar 27;25(3):bbae133. doi: 10.1093/bib/bbae133.ABSTRACTPeptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this fi...
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Le Zhang Wenkai Song Tinghao Zhu Yang Liu Wei Chen Yang Cao Source Type: research
Integrated modeling of protein and RNA
Brief Bioinform. 2024 Mar 27;25(3):bbae139. doi: 10.1093/bib/bbae139.NO ABSTRACTPMID:38561980 | DOI:10.1093/bib/bbae139 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 2, 2024 Category: Bioinformatics Authors: Haoquan Liu Yunjie Zhao Source Type: research
IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability
Brief Bioinform. 2024 Mar 27;25(3):bbae080. doi: 10.1093/bib/bbae080.ABSTRACTLung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show t...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Zhanyu Xu Haibo Liao Liuliu Huang Qingfeng Chen Wei Lan Shikang Li Source Type: research
IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data
We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.PMID:38557673 | PMC:PMC10982947 | DOI:10.1093/bib/bbae128 (Source: ...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Marc-Antoine Gerault Samuel Granjeaud Luc Camoin P är Nordlund Lingyun Dai Source Type: research
SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data
Brief Bioinform. 2024 Mar 27;25(3):bbae129. doi: 10.1093/bib/bbae129.ABSTRACTQuality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such ra...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Jinze Huang Yang Zhao Bo Meng Ao Lu Yaoguang Wei Lianhua Dong Xiang Fang Dong An Xinhua Dai Source Type: research
SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding
In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Wang Yin You Wan Yuan Zhou Source Type: research
Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology
Brief Bioinform. 2024 Mar 27;25(3):bbae131. doi: 10.1093/bib/bbae131.ABSTRACTUnderstanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Theinmozhi Arulraj Hanwen Wang Alberto Ippolito Shuming Zhang Elana J Fertig Aleksander S Popel Source Type: research
Graphormer supervised de novo protein design method and function validation
Brief Bioinform. 2024 Mar 27;25(3):bbae135. doi: 10.1093/bib/bbae135.ABSTRACTProtein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inade...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Junxi Mu Zhengxin Li Bo Zhang Qi Zhang Jamshed Iqbal Abdul Wadood Ting Wei Yan Feng Hai-Feng Chen Source Type: research
