2020年7月1日 · In this paper, DTA has been achieved using three different approaches: (i) by establishing PDUE conditions in subregion-level, (ii) by establishing DSO conditions in subregion-level, and (iii) by providing travelers with route guidance (RG) information based on DSO conditions in region-level, where the route guidance commands are applied in ...

• Explain the basic concepts of DTA and various DTA definitions and implementations, • Highlight the types of transportation analysis applications for which DTA models could be found useful, • Provide information about how to select a DTA model that best serves the intended application,

2025年1月28日 · This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction.

2025年1月20日 · We propose DHAG-DTA, a general dynamic hierarchical affinity graph DNN approach, for DTA prediction using molecular sequence information and already known drug-target interactions.

This guidebook explains how to approach the development and application of a transportation model with DTA for alternatives analysis. Dynamic Traffic Assignment is an evolving technique in transportation modeling.

2025年1月7日 · To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs.

2025年1月20日 · In this paper, we propose a novel multimodal deep learning model named MMPD-DTA for predicting drug-target binding affinity to address these challenges. The MMPD-DTA model integrates graph and sequence modalities of targets, pockets, and drugs to capture both global and local target and drug information.

2020年10月24日 · We propose a novel deep learning model called GraphDTA for drug–target affinity (DTA) prediction. We frame the DTA prediction problem as a regression task where the input is a drug–target pair and the output is a continuous measurement of …

2023年1月27日 · We propose a novel Multi-Functional and Robust Drug–Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements.

DynusT is the most widely used simulation-based Dynamic Traffic Assignment (DTA) model with more than 200 projects/publications by users worldwide due to its superior traffic simulation realism and computational efficiency.