Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and grap...
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Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this critical gap, this study introduces DeepTracer-LowResEnhance, an innovative computational framework that uniquely integrates structural predictions from AlphaFold with a deep-learning-based map refinement strategy specifically tailored to enhance low-resolution maps. Unlike existing techniques, our approach leverages the strengths of AlphaFold’s sequence-based predictions combined with advanced neural network-driven refinement processes to significantly improve map interpretability and modeling accuracy. DeepTracer-LowResEnhance demonstrates substantial and consistent improvements on an extensive dataset comprising 37 diverse protein cryo-EM maps, covering resolutions from 2.5 to 8.4 Å and including 22 challenging cases below 4 Å resolution. DeepTracer-LowResEnhance achieves an average TM-score improvement of 3.53x compared to baseline DeepTracer predictions. Notably, our enhanced methodology showed performance gains across 95.5% of the tested low-resolution datasets. A comparative analysis alongside traditional sharpening methods such as Phenix’s auto-sharpening illustrates DeepTracer-LowResEnhance’s superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.
Concept index (CI) is a very fast and efficient feature extraction (FE) algorithm for text classification. The key approach in CI scheme is to express each document as a function of various concepts (centroids) ...
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Concept index (CI) is a very fast and efficient feature extraction (FE) algorithm for text classification. The key approach in CI scheme is to express each document as a function of various concepts (centroids) present in the collection. However,the representative ability of centroids for categorizing corpus is often influenced by so-called model misfit caused by a number of factors in the FE process including feature selection to similarity measure. In order to address this issue, this work employs the "DragPushing" Strategy to refine the centroids that are used for concept index. We present an extensive experimental evaluation of refined concept index (RCI) on two English collections and one Chinese corpus using state-of-the-art Support Vector Machine (SVM) classifier. The results indicate that in each case, RCI-based SVM yields a much better performance than the normal CI-based SVM but lower computation cost during training and classification phases.
Landmark recognition is a useful yet challenging task due to the lack of large annotated landmark datasets. Previous related work often neglects non-English speaking areas, such as Chinese landmarks. Tests using comme...
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Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular ...
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In this article, we propose a new multiscale sliding window model which differentiates data items in different time periods of the data stream, based on a reasonable monotonicity of resolution assumption. Our model, a...
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Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of ...
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While tracking data and software provenance is necessary in eScience, and often implemented in scientific workflow management tools, such tools generally don't provide graphical UIs that use provenance to guide wo...
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Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Al...
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Currently, mental health issues are increasing among individuals. Sharing feelings with someone who cares plays a key role in resolving these issues. Virtual assistants that can simulate human conversations using arti...
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ISBN:
(数字)9798350383737
ISBN:
(纸本)9798350383744
Currently, mental health issues are increasing among individuals. Sharing feelings with someone who cares plays a key role in resolving these issues. Virtual assistants that can simulate human conversations using artificial intelligence can be effectively used to communicate with individuals facing challenges. This paper describes iCare, a conversational AI that uses machine learning to supply support to individuals suffering from mental health issues. iCare supplies a safe, private, virtual environment for users to share their feelings, and get empathetic response that improves their mental health. It combines multiple techniques to provide the best responses to the users. iCare is designed to provide support for a range of users including those who are suffering from anxiety or depression, and individuals who are unhappy and need help to improve their present feeling.
Genes are sequences of nucleotide in DNA that encode proteins. Essential genes are a type of genes that are critical and indispensable for an organism’s survival. Many network-based algorithms have been developed to ...
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