AIMC Topic: Algorithms

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AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.

Journal of computer-aided molecular design
Protein pockets, or small cavities on the protein surface, are critical sites for enzymatic catalysis, molecular recognition, and drug binding. Accurately identifying these pockets is crucial for understanding protein function and designing therapeut...

KGMP: Augmenting retrieval knowledge graph with multi-hop perceptron.

PloS one
The core challenge of Knowledge Base Question Answering (KBQA), as a bridge between natural language and structured knowledge, is to accurately map complex semantic queries into Graph Query Language (GQL). Compared with the traditional Text-to-SQL ta...

Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction.

PloS one
Maximizing information transfer across different structural scales is critical for effective molecular representation learning. Current molecular graph neural networks fail to fully capture the multi-scale nature of molecular geometry, leading to sub...

A poisson flow-based data augmentation and lightweight diagnosis framework for imbalanced rolling bearing faults.

PloS one
Accurate diagnosis of rolling bearing faults is vital for the safe operation of rotating machinery. However, real-world fault datasets often suffer from severe class imbalance, which hinders the performance of deep learning models. To address this ch...

Predicting six-month mortality in adult hemophagocytic lymphohistiocytosis with machine learning: a prognostic approach utilizing laboratory data.

Annals of medicine
BACKGROUND: Hemophagocytic lymphohistiocytosis (HLH) is associated with high mortality rates. This study was conducted to develop and validate a predictive model for adult HLH patients at high risk of six months mortality using machine learning (ML) ...

Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.

Journal of chemical information and modeling
Network methods and molecular dynamics (MD) simulations have become essential tools for studying protein dynamics. However, applying network methods to MD simulations of flexible proteins is a major challenge, since the high conformational heterogene...

Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods.

BMC surgery
OBJECTIVE: The presence of pancreaticobiliary maljunction (PBM) in pediatric patients with congenital choledochal malformation significantly impacts clinical management and surgical decision-making. Current preoperative evaluation of PBM coexistence ...

Energy-efficient clustering and routing for IoT-enabled healthcare using adaptive fuzzy logic and hybrid optimization.

Scientific reports
Leveraging Internet of Things technology in healthcare, including wireless sensor networks and next-generation networks, enhances the seamless integration of medical equipment and enables intelligent interaction among devices. This advancement plays ...

Clinician perspectives on explainability in AI-driven closed-loop neurotechnology.

Scientific reports
Artificial Intelligence (AI) holds promise for advancing the field of neurotechnology and accelerating its clinical translation. AI-driven clinical neurotechnologies leverage the power of non-linear algorithms to analyze complex brain data and enable...

GENEOnet: a breakthrough in protein binding pocket detection using group equivariant non-expansive operators.

Scientific reports
Structure-based virtual screening approaches like molecular docking rely on accurately identifying and precisely calculating binding pockets to efficiently search for potential ligands. In this paper, we introduce GENEOnet, a machine learning model d...