Abstract: Disclosed is a method for convolution calculation in a neural network, comprising: reading an input feature map, depthwise convolution kernels and pointwise convolution kernels from a dynamic random access memory (DRAM); performing depthwise convolution calculations and pointwise convolution calculations by depthwise convolution calculation units and pointwise convolution calculation units, according to the input feature map, the depthwise convolution kernels and the pointwise convolution kernels to obtain output feature values of a first predetermined number p of points on all pointwise convolution output channels; storing the output feature values of a first predetermined number p of points on all pointwise convolution output channels into an on-chip memory; and repeating above operation to obtain output feature values of all points on all point wise convolution output channels. Therefore, the storage space for storing intermediate results may be reduced.

Abstract: Disclosed is an apparatus for performing a convolution operation in a convolutional neural network. The apparatus may comprise a selector for selecting one or more nonzero elements of a weight parameter, a selector for selecting a data item(s) corresponding to selected nonzero elements in input feature data, and a calculator unit for performing an operation. The apparatus may realize the convolution operation in a sparsified convolutional neural network efficiently through the hardware.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.

Abstract: A system framework and method for predicting adverse drug reactions (ADRs). Structures represented in three-dimensions were prepared for small drug molecules and unique human proteins and binding scores between them were generated using molecular docking. Machine learning models were developed using the molecular docking features to predict ADRs. Using the machine learning models, it can successfully predict a drug-induced ADR based on drug- target interaction features and known drug-ADR relationships. By further analyzing the binding proteins that are top ranked or closely associated with the ADRs, there may be found possible interpretation of the ADR mechanisms. The machine learning ADR models based on molecular docking features not only assist with ADR prediction for new or existing known drug molecules, but also have the advantage of providing possible explanation or hypothesis for the underlying mechanisms of ADRs.

Abstract: A method for predicting adverse drug reactions (ADRs). Structures represented in three-dimensions were prepared for small drug molecules and unique human proteins and binding scores between them were generated using molecular docking. Machine learning models were developed using the molecular docking features to predict ADRs. Using the machine learning models, it can successfully predict a drug-induced ADR based on drug-target interaction features and known drug-ADR relationships. By further analyzing the binding proteins that are top ranked or closely associated with the ADRs, there may be found possible interpretation of the ADR mechanisms. The machine learning ADR models based on molecular docking features not only assist with ADR prediction for new or existing known drug molecules, but also have the advantage of providing possible explanation or hypothesis for the underlying mechanisms of ADRs.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

Abstract: Embodiments of the present invention disclose a method and an apparatus of establishing an image search relevance prediction model, and an image search method and apparatus. The method of establishing an image search relevance prediction model comprises: training a pre-constructed original deep neural network by using a training sample, wherein the training sample comprises: a query and image data, and the original deep neural network comprises: a representation vector generation network and a relevance calculation network; and using the trained original deep neural network as the image search relevance prediction model. The technical solution of the present invention optimizes the existing image search technology, and has stronger capabilities than the prior art as well as various integrations and variations in terms of semantic matching between a query and an image text, semantic matching between a query and image content, click generalization and the like.

Abstract: A mobile terminal and an antenna apparatus therefor are provided. The antenna apparatus comprises an antenna body arranged at a first position in a space between the display case and the display screen; and a reference ground connected to the antenna body and arranged at a second position in the space, the reference ground having an electromagnetic band gap structure. With such a structure, it is possible to effectively improve the radiation efficiency of the antenna without increasing the size of the antenna apparatus. Thus, the antenna apparatus can satisfy the design requirement on portability of the mobile terminal.