Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

Xi, Jianing; Yuan, Xiguo; Wang, Minghui; Li, Ao; Li, Xuelong; Huang, Qinghua

doi:10.1093/bioinformatics/btz793

Cited by 58 publications

(42 citation statements)

References 41 publications

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“…We test PGMicroD based on both simulation and real datasets, indicating the PGMicroD exhibits superior performance. In future work, we intend to integrate mutations such as single nucleotide variations (Yuan et al, 2020) and copy number variations (Xi et al, 2019;Zhao et al, 2020) to improve the detection of microbial composition. We also plan to establish a more comprehensive reference library for detecting species and improving detection accuracy and create new methods aiming at the filtered reads to identify new species.…”

Section: Discussionmentioning

confidence: 99%

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

2020

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Next-generation sequencing (NGS) technologies have provided great opportunities to analyze pathogenic microbes with high-resolution data. The main goal is to accurately detect microbial composition and abundances in a sample. However, high similarity among sequences from different species and the existence of sequencing errors pose various challenges. Numerous methods have been developed for quantifying microbial composition and abundance, but they are not versatile enough for the analysis of samples with mixtures of noise. In this paper, we propose a new computational method, PGMicroD, for the detection of pathogenic microbial composition in a sample using NGS data. The method first filters the potentially mistakenly mapped reads and extracts multiple species-related features from the sequencing reads of 16S rRNA. Then it trains an Support Vector Machine classifier to predict the microbial composition. Finally, it groups all multiple-mapped sequencing reads into the references of the predicted species to estimate the abundance for each kind of species. The performance of PGMicroD is evaluated based on both simulation and real sequencing data and is compared with several existing methods. The results demonstrate that our proposed method achieves superior performance. The software package of PGMicroD is available at https://github.com/BDanalysis/PGMicroD.

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Section: Discussionmentioning

confidence: 99%

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

2020

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“…In addition, we performed GO and KEGG enrichment analyses, and the result showed there were no significant enrichments. Moreover, on the GO and KEGG terms (Jiao et al, 2012;Xi et al, 2020), there are also no correlations between these signature genes and radiation. Maybe these genes were novel candidate targets and biomarkers correlated with radiation.…”

Section: Comparison With the Gene Signatures Based On Single Omics Datamentioning

confidence: 97%

Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data

et al. 2020

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Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high-and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.

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“…Different feature map channels in each convolution stage can be regarded as different feature representations [46,47]. A large number of channels are applied to represent different feature maps, but this results in several meaningless feature channels.…”

Section: Channel-wise Attention Modulementioning

confidence: 99%

Triple-Attention-Based Parallel Network for Hyperspectral Image Classification

Zhu

Zheng

et al. 2021

Remote Sensing

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Convolutional neural networks have been highly successful in hyperspectral image classification owing to their unique feature expression ability. However, the traditional data partitioning strategy in tandem with patch-wise classification may lead to information leakage and result in overoptimistic experimental insights. In this paper, we propose a novel data partitioning scheme and a triple-attention parallel network (TAP-Net) to enhance the performance of HSI classification without information leakage. The dataset partitioning strategy is simple yet effective to avoid overfitting, and allows fair comparison of various algorithms, particularly in the case of limited annotated data. In contrast to classical encoder–decoder models, the proposed TAP-Net utilizes parallel subnetworks with the same spatial resolution and repeatedly reuses high-level feature maps of preceding subnetworks to refine the segmentation map. In addition, a channel–spectral–spatial-attention module is proposed to optimize the information transmission between different subnetworks. Experiments were conducted on three benchmark hyperspectral datasets, and the results demonstrate that the proposed method outperforms state-of-the-art methods with the overall accuracy of 90.31%, 91.64%, and 81.35% and the average accuracy of 93.18%, 87.45%, and 78.85% over Salinas Valley, Pavia University and Indian Pines dataset, respectively. It illustrates that the proposed TAP-Net is able to effectively exploit the spatial–spectral information to ensure high performance.

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Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

Cited by 58 publications

References 41 publications

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data

Triple-Attention-Based Parallel Network for Hyperspectral Image Classification

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