Events Search and Views Navigation

Event Views Navigation

Today

Drug Target Interaction

Drug discovery remains a slow and expensive process which involves many steps, from detecting the target structure to being Food and Drug Administration (FDA) approved, and is often riddled with safety concerns. Accurately predicting how drugs interact with their targets and developing new drugs by using better methods and technologies holds immense potential to speed up this process, ultimately leading to faster delivery of life-saving medications. Traditional methods for drug-target interaction prediction have limitations, particularly in capturing the complex relationships between drugs and their targets. As an outcome, deep learning models have been introduced to overcome the challenges of interaction prediction with accurate and fast results.

Featured Featured

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Foundation models, primarily based on Transformers, struggle with computational inefficiency on long sequences. To address this, Mamba is introduced, a new model architecture that replaces attention with selective state space models (SSMs). Mamba enables content-based reasoning, achieves linear scaling with sequence length, and surpasses Transformers in performance across various modalities, including language, audio, and genomics.

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

In this presentation, a novel approach to anomaly detection using Graph Neural Networks (GNNs) is introduced. This method models multivariate time series as a graph, learns sensor relationships and detects anomalies by identifying deviations from these learned patterns. By capturing both temporal dependencies and sensor interactions, proposed approach enhances anomaly detection, offering superior performance compared to traditional techniques.

Sentimentally enhanced conversation recommender system

A conversation recommender system (CRS) aims to provide high-quality recommendations to users in fewer conversation turns. Existing studies often rely on knowledge graphs and entities mentioned in dialogues to enhance the representation of entity information. However, they fail to thoroughly explore users’ emotional tendencies toward entities or effectively differentiate the varying impacts of different entities on user preferences. Recently, Liu et.al. proposed an innovative Sentimentally Enhanced Conversation Recommender system (SECR). In this presentation, the SECR method will be explained and described how this method captured users' emotional inclinations toward entities.