MOTIVATION: Drug repositioning, identifying new therapeutic applications for existing drugs, can significantly reduce the time and cost involved in drug development. Recent studies have explored the use of Anatomical Therapeutic Chemical (ATC) codes in drug repositioning, offering a systematic framework to predict ATC codes for a drug. The ATC classification system organizes drugs according to their chemical properties, pharmacological actions, and therapeutic effects. However, its complex hierarchical structure and the limited scalability at higher levels present significant challenges for achieving accurate ATC code prediction. RESULTS: We propose a novel approach to predict ATC codes of drugs, named Feature Aggregation and Convolution with Transformer models (FACT). This method computes three types of drug similarities, incorporating ATC code similarity with hierarchical weights and masked drug-ATC code associations. These features are then aggregated for each target drug-ATC code pair and processed through a convolution-transformer encoder to generate three embeddings. The embeddings are finally used to estimate the probability of an association between the target pair. The experimental results demonstrate that the proposed method achieves an area under the receiver operating characteristic curve (AUROC) of 0.9805 and an area under the precision-recall curve of 0.9770 at level 4 of the ATC codes, outperforming the previous methods by 15.05% and 18.42%, respectively. This study highlights the effectiveness of integrating diverse drug features and the potential of transformer-based models in ATC code prediction. AVAILABILITY AND IMPLEMENTATION: Source code of FACT is freely available at https://github.com/knhc1234/FACT.

Predicting disease-gene associations is crucial in human health for understanding the genetic basis of diseases, identifying potential therapeutic targets, and advancing personalized medicine. In this paper, we present a novel network-based method that integrates graph neural networks (GNNs), attention mechanisms, and random walks to accurately predict diseasegene associations. GNNs encode structural features to nodes in a heterogeneous graph, attention mechanisms compute the connectivity between the nodes, and random walks capture the changes between states within the graph. Experimental results demonstrated the superiority of the proposed model, achieving the area under the receiver operating characteristics curve of 0.982 and recall 0.907.

This paper describes the architecture and functions of the audio processing unit (APU) which deals with linear pulse code modulation (PCM) audio signals in the multipoint control unit (MCU), that used for a multipoint video conferencing system. The MCU receives audiovisual data from the subscriber, carries out video merging and audio mixing, and distributes the data to them. It also performs session setup, tear down, and control of the conference. The audio processing unit consists of a local processor and peripheral memories, a VME bus control block, a current speaker detection block, and an audio-mixing block. The current speaker detection block determines the channel of the current speaker by comparing the mean power of each channel. A mixing block mixes linear PCM audio data using speaker information and, performs echo cancellation. We propose another speaker detection algorithm using the scale factor of the MPEG-1 audio elementary stream. In simulation, it showed a similar result in speaker detection with that of linear PCM audio.