Papers Activity Prediction
“Activity Prediction” 태그가 달린 논문 137편 · 필터 해제
ProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases
Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically ''undruggable'' targets via the ubiquitin-proteasome system. Despite growing efforts t…
Graph Neural NetworkActivity PredictionBeyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression
Deep Imbalanced Regression (DIR) is pervasive in continuous prediction tasks across diverse modalities, such as age estimation, depth prediction, and protein mutation activity prediction, where label-scarce tail samples …
Contrastive LearningActivity PredictionAge EstimationCoarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalize…
Activity PredictionMonroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research …
Activity PredictionMulti-Task LearningRevisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches
Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next …
Activity PredictionStable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offering a practical route to quantum sequence…
Activity PredictionEHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction
Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur. Most existing methods assume classical single-case event logs, whereas real ser…
Activity PredictionDavid vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM
Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. The PPM field has progressed from classica…
Activity PredictionDIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining
Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-entropy optimize local next-step likelihoods and only …
Activity PredictionBiological Reasoning-Informed Regression for Interpretable Regulatory DNA Activity Prediction
DNA cis-regulatory elements (CREs) such as enhancers control gene expression levels. Accurately predicting regulatory activity from DNA sequences is valuable but challenging, as it requires understanding complex biologic…
Activity PredictionFrequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. We propose FLaG, a plug-in aggregation mo…
Image ClassificationText ClassificationActivity PredictionMos-Gen: A Generative Molecular Framework for Mosquito Insecticide Design
Mosquito-borne infectious diseases cause more than 700000 deaths worldwide each year. The long-term use of conventional chemical insecticides has induced serious resistance problems, creating an urgent need to develop no…
Activity PredictionAMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI
Wi-Fi-based human activity recognition (HAR) has emerged as a promising approach for contactless sensing, leveraging channel state information (CSI) collected from wireless transceivers. While existing studies have prima…
Human Activity RecognitionActivity PredictionDecomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling
Machine-learning predictors of biochemical activity often exhibit large random-split-to-leave-one-target-out generalisation gaps that have been documented but not decomposed. We frame this as an evaluation-science questi…
Activity PredictionDo Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminform…
Activity PredictionDrug DiscoveryPromoting Simple Agents: Ensemble Methods for Event-Log Prediction
We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event logs. Experiments on synthetic patterns and five real-world process min…
Activity PredictionChameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction
Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, cur…
Activity PredictionContinual LearningDPD-Cancer: Explainable Graph-Based Deep Learning for Small Molecule Anti-Cancer Activity Prediction
DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule anti-cancer activity across the NCI-60 panel, tr…
Activity PredictionValue predictionMMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introd…
Activity PredictionDrug DiscoveryEVA: Towards a universal model of the immune system
The effective application of foundation models to translational research in immune-mediated diseases requires multimodal patient-level representations that can capture complex phenotypes emerging from multicellular inter…
Activity PredictionTransfer Learning