Associative learning mechanism for drug‐target interaction prediction

As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction model,which enhances the consistency of the probability distribution between actual and predicted values.The source code of proposed method is available at https://github.com/Lamouryz/Code/tree/main/MT‐DTA.

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

As one chemical composition,nicotine content has an important influence on the quality of tobacco leaves.Rapid and nondestructive quantitative analysis of nicotine is an important task in the tobacco industry.Near-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely used.In this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud environment.This 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy data.It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches.This research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.

Deep Neural Network-based Speaker-Aware Information Logging for Augmentative and Alternative Communication

People with complex communication needs can use a high-technology augmentative and alternative communication device to communicate with others.Currently,researchers and clinicians often use data logging from high-tech augmentative and alternative communication devices to analyze augmentative and alternative communication user performance.However,existing automated data logging systems cannot differentiate the authorship of the data log when more than one user accesses the device.This issue reduces the validity of the data logs and increases the difficulties of performance analysis.Therefore,this paper presents a solution using a deep neural network-based visual analysis approach to process videos to detect different augmentative and alternative communication users in practice sessions.This approach has significant potential to improve the validity of data logs and ultimately to enhance augmentative and alternative communication outcome measures.