Hematology

Lymphoma

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

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Toward Self-Propelled Microrobots: A Systems Chemistry that Induces Non-Linear Phenomena of Oil Droplets in Surfactant Solution.

Biological activities observed in living systems occur as the output of which nanometer-, submicrome...

Could metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need.

Pre-implantation genetic testing for aneuploidy (PGT-A) via embryo biopsy helps in embryo selection ...

Enhancing beer authentication, quality, and control assessment using non-invasive spectroscopy through bottle and machine learning modeling.

Fraud in alcoholic beverages through counterfeiting and adulteration is rising, significantly impact...

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offe...

[Rapid non-destructive detection technology for traditional Chinese medicine preparations based on machine learning: a review].

In recent years, with the increasing societal focus on drug quality and safety, quality issues have ...

Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity ...

Mitigating epidemic spread in complex networks based on deep reinforcement learning.

Complex networks are susceptible to contagious cascades, underscoring the urgency for effective epid...

Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).

In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) ...

[HIPPOCRATES AND LANGUAGE MODELS - PRIMUM NON NOCERE - FIRST, DO NO HARM].

For millennia, the ethos of "First, Do No Harm", attributed to Hippocrates, has been a cornerstone o...

Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction.

Predicting molecular processes using deep learning is a promising approach to provide biological ins...

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

PURPOSE: Limited studies have used natural language processing (NLP) in the context of non-small cel...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tong...

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

OBJECTIVES: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medic...

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive ...

Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging.

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruct...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our abili...

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenet...

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