Learning to detect drug-drug interactions based on Ensemble of Models


Learning to detect drug-drug interactions based on Ensemble of Models – We propose a novel approach to the study of brain function in association with multiple domains — as is the case in many medical applications. We provide a framework for analyzing the structural basis of association by learning from the correlations among brain function patterns. We build on recent approaches to learning from brain functional association patterns as well as learning from multiple associations between brain function patterns, and we show that our framework is able to learn the relationships among brain functions, and provide useful computational tools for understanding association structures.

We propose a novel deep learning based method for human recognition of a single point in biological data. To solve this challenge, a deep learning formulation that uses a high-level semantic segmentation of the visual system is proposed. This formulation is used as training data for a multi-view 3D face recognition system which incorporates visual information and a temporal segmentation. We evaluated the proposed method on the ImageNet dataset in a clinical setting, and achieved a COCO score of 0.82, which is the best accuracy achieved by any single person on a dataset of human face images.

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Learning to detect drug-drug interactions based on Ensemble of Models

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  • On the Existence and Negation of Semantic Labels in Multi-Instance Learning

    An efficient method for multi-view descriptor generation for biomedical dataWe propose a novel deep learning based method for human recognition of a single point in biological data. To solve this challenge, a deep learning formulation that uses a high-level semantic segmentation of the visual system is proposed. This formulation is used as training data for a multi-view 3D face recognition system which incorporates visual information and a temporal segmentation. We evaluated the proposed method on the ImageNet dataset in a clinical setting, and achieved a COCO score of 0.82, which is the best accuracy achieved by any single person on a dataset of human face images.


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