Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Advancing precision and personalized breast cancer treatment through multi-omics technologies.

Breast cancer is the most common malignant tumour in women, with more than 685,000 women dying of br...

A fully value distributional deep reinforcement learning framework for multi-agent cooperation.

Distributional Reinforcement Learning (RL) extends beyond estimating the expected value of future re...

Multibody system dynamics for bio-robotic design and simulation based on inching-locomotion caterpillar's gait: MBD-ILAR method.

Inching-locomotion caterpillars (ILAR) inspire the design of 'inch-worm' robots with biomimicry feat...

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.

BACKGROUND: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution...

Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.

Proteins associated with multiple diseases often interact, forming disease modules that are critical...

Integrated multi-omics analysis describes immune profiles in ischemic heart failure and identifies PTN as a novel biomarker.

INTRODUCTION: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF...

Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization.

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a ...

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the compl...

Enhancing Nitrogen Nutrition Index estimation in rice using multi-leaf SPAD values and machine learning approaches.

Accurate nitrogen diagnosis is essential for optimizing rice yield and sustainability. This study in...

MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identi...

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.

Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses fro...

AGML: Adaptive Graph-Based Multi-Label Learning for Prediction of RBP and as Event Associations During EMT.

Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediati...

A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics.

Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important r...

ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks.

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes th...

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targe...

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This stu...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immu...

M4Net: Multi-level multi-patch multi-receptive multi-dimensional attention network for infrared small target detection.

The detection of infrared small targets is getting more and more attention, and has a wider applicat...

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