This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associa...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://***/ZiyuFanCSU/LSNSCDA.
bioinformatics workloads differ significantly from traditional scientific computing and AI workloads because they consist primarily of integer-only operations and string comparisons rather than floating-point operatio...
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ISBN:
(数字)9798350355543
ISBN:
(纸本)9798350355550
bioinformatics workloads differ significantly from traditional scientific computing and AI workloads because they consist primarily of integer-only operations and string comparisons rather than floating-point operations. The underlying algorithms usually have low arithmetic intensity, irregular memory access patterns, and non-deterministic workloads. Local Assembly is an essential step in large-scale genome assembly software and is typically implemented using de Bruijn graphs. This paper examines the performance, portability, and productivity of a local assembly GPU kernel from a metagenome assembly pipeline implemented using hash table data structures on NVIDIA, AMD, and Intel GPUs. We focus on the challenges of achieving portability while maintaining performance for a complex bioinformatics GPU kernel that relies on hardware-specific optimizations. In this paper, we evaluate the local assembly kernel's performance and portability across different GPU architectures, identify performance bottlenecks, and propose modifications in existing tools and methods for performance modeling and analysis of integer-heavy bioinformatics application kernels.
The advent of data-driven science and artificial intelligence (AI) has provided a deeper knowledge about data that has driven the clinical research to unprecedented change. AI has shown great potential in the scoliosi...
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We present hybrid system based gene regulation models of mammalian circadian cycle and the results of model behaviour analysis. The models cover genes of two recently proposed biological models with 5 and 3 gene '...
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ISBN:
(纸本)9789897585524
We present hybrid system based gene regulation models of mammalian circadian cycle and the results of model behaviour analysis. The models cover genes of two recently proposed biological models with 5 and 3 gene 'core oscillators'. The advantage of the used HSM framework is limited model dependence on parameter values, which are described only at qualitative level at the extent they affect models' observable behaviour. The models represent gene regulatory networks in terms of genes, proteins, binding sites, regulatory functions, and constraints on growth rates and binding affinities. Although such models do provide limited accuracy, they are less dependent from parameter fitting and can provide predictions on some biological aspects of gene regulation that are not dependent form the choice of particular parameter values. The models can provide biologically feasible predictions about synchronised oscillation of the involved genes and functions that regulate gene activity on basis of regulatory network topology alone. The work also includes developments of new analysis methods, in particular, for analysis of available trajectories in HSM state spaces and derivation of constraints that are needed for state transition trajectories to satisfy the required specific properties.
Single-cell RNA sequencing (scRNA-seq) technology offers unprecedented opportunities for inferring gene regulatory networks (GRNs) at the genome level. However, scRNA-seq data is highly sparse and has a low signal-to-...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Single-cell RNA sequencing (scRNA-seq) technology offers unprecedented opportunities for inferring gene regulatory networks (GRNs) at the genome level. However, scRNA-seq data is highly sparse and has a low signal-to-noise ratio with significant dropout. Many unsupervised or self-supervised models have been proposed to infer GRNs from large RNA-seq datasets, but few are suitable for scRNA-seq data. Recent research confirms that transcription factor (TF)-DNA binding data enables supervised GRN inference. In this paper, we propose a novel framework called GRNNLink, which leverages known GRNs to infer potential regulatory relationships between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce an interactive graph encoder based on a graph recurrent neural network (GRNN) to refine gene features by capturing the correlations between network nodes. Finally, matrix completion is performed using the node correlation features to predict GRNs. To evaluate model performance, we compare GRNNLink with six existing GRN reconstruction methods across seven scRNA-seq datasets. The results demonstrate that our method exhibits high robustness and accuracy.
Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alte...
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The majority of spatial transcriptomics datasets are characterized by low resolution, wherein each spot generally encompasses multiple cells. This limitation poses challenges for exploring biological insights at the c...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
The majority of spatial transcriptomics datasets are characterized by low resolution, wherein each spot generally encompasses multiple cells. This limitation poses challenges for exploring biological insights at the cellular level. Consequently, the development and application of robust deconvolution methods for spatial transcriptomics data are imperative to address this challenge. Addressing the limitations of previous deconvolution methods—such as the lack of consideration cell type labels from single-cell sequencing data and the inability to adaptively capture local relationship among points—we propose a novel spatial transcriptomics data deconvolution model based on label-guided Multi-Head Dynamic Graph Attention Networks with Optimal Transport(MHDGATOT). Our approach leverages an advanced multi-head dynamic graph attention network to adaptively capture inter-data relationships and generate effective low-dimensional embeddings. Subsequently, we employ optimal transport based on fused gromov-wasserstein to derive the transport matrix between spatial transcriptomics data and single-cell sequencing data, facilitating the accurate deconvolution of spatial transcriptomics datasets. Experimental validation substantiates the effectiveness of our model.
Monoclonal antibodies provide targeted treatment options for various diseases. In infectious diseases, techniques like reverse vaccinology 2.0 can extract potent monoclonal antibodies from human donors. However, there...
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Volumetric fluorescence microscopy is crucial for non-invasive three-dimension (3D) visualization of biological systems but faces challenges due to anisotropic blurring caused by the point spread function (PSF). Previ...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Volumetric fluorescence microscopy is crucial for non-invasive three-dimension (3D) visualization of biological systems but faces challenges due to anisotropic blurring caused by the point spread function (PSF). Previous methods have struggled with adapting to the diverse PSFs and have not effectively addressed their overall impacts of PSF. We propose Isotropic Diffusion Posterior Sampling (IsotropicDPS), solving isotropic reconstruction as a blind inverse problem. Our method employs two specialized score-based diffusion models, each trained on high-resolution lateral images and a diverse set of blurring PSFs. This approach enables the joint estimation of both the clean axial images and the PSF through a conditional posterior sampling strategy with a parallel reverse diffusion process. Remarkably, IsotropicDPS achieves zero-shot reconstruction and PSF estimation without requiring axial images during training. We validated our method through experiments on synthetic and real data, demonstrating superior performance and adaptability to varying PSF scenarios compared to existing methods.
Protein sequence classification is a crucial task in the field of bioinformatics as it helps to reveal various types of properties of proteins. Machine learning and deep learning algorithms have great application valu...
ISBN:
(纸本)9798400709449
Protein sequence classification is a crucial task in the field of bioinformatics as it helps to reveal various types of properties of proteins. Machine learning and deep learning algorithms have great application value in the problem of protein classification prediction. In this study, we propose a novel approach for extracting sequence features based on N-Gram model. We then utilize this approach to train and evaluate machine learning and deep learning algorithms on the same dataset. The experimental results show that the Random Forest method based on N-Gram features significantly outperforms other types of algorithmic models on this dataset, with a further increase in accuracy.
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