Breast cancer is the world's second-largest cause of cancer mortality among women. With the progress of artificial intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent y...
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Acute hemorrhage in pregnancy may lead to maternal and/or fetal morbidity or mortality. In emergency medicine, blockage of the aorta via an inflatable endovascular balloon, technically referred to Resuscitative Endova...
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Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription...
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Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dy-namics or where rates of transcription and splicing are time dependent. We present "LatentVelo,"an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based"latent space with neural ordinary differential equations. LatentVelo infers a latent reg-ulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can pre-dict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperform-ing comparable autoencoder batch correction methods that do not consider gene expression dynamics.
This paper proposes a method named AE-ACG for stock price movement prediction. In AE-ACG, the convolutional neural network (CNN) and gated recurrent unit (GRU) are combined to design a base layer, which is embedded in...
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This paper proposes a method named AE-ACG for stock price movement prediction. In AE-ACG, the convolutional neural network (CNN) and gated recurrent unit (GRU) are combined to design a base layer, which is embedded in the autoencoder (AE) framework, to efficiently extract features from financial time series data. Furthermore, skip connection links encoding and decoding to leverage hierarchical features. Attention mechanism (AM) also distinguishes the importance of historical data across periods. Extensive experiments demonstrated that the proposed model is effective in predicting price movements, showing advantages over some mainstream methods.
Objective: We aimed to develop a deep learning (DL) -based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its ...
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Objective: We aimed to develop a deep learning (DL) -based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness ***: Developing and evaluating an artificial intelligence detection ***: Pretraining paired data of color fundus photographs and SD-OCT images from 189 healthy par-ticipants and 371 patients with early glaucoma were ***: The variational autoencoder (VAE) network training architecture was used for training, and the correlation between the fundus photographs and RNFL thickness distribution was determined through the deep neural network. The reference standard was defined as a vertical cup-to-disc ratio of >0.7, other typical changes in glaucomatous optic neuropathy, and RNFL defects. Convergence indicates that the VAE has learned a dis-tribution that would enable us to produce corresponding synthetic OCT *** Outcome Measures: Similarly to wide-field OCT scanning, the proposed model can extract the results of RNFL thickness analysis. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to assess signal strength and the similarity in the structure of the color fundus images converted to an RNFL thickness distribution model. The differences between the model-generated images and original images were quantified. Results: We developed and validated a novel DL-based algorithm to extract thickness information from the color space of fundus images similarly to that from OCT images and to use this information to regenerate RNFL thickness distribution images. The generated thickness map was sufficient for clinical glaucoma detection, and the generated images were similar to ground truth (PSNR: 19.31 decibels;SSIM: 0.44). The inference results were similar to the OCT-generated original images in te
There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype...
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There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https:// ***/web/packages/iSubGen.
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