Latest Articles for Personalized Medicine

A deeper look into effective methods for analyzing health outcomes over time.

PCKGs offer a clearer view of patient health for better care.

Studies reveal links between protein levels and disease risks.

Enhancing cancer care through tailored patient monitoring and decision-making models.

Combining knowledge graph embedding and case-based reasoning for better treatments.

This article discusses methods for handling missing data in diabetes studies using CGM.

Improving disease classification for better patient treatment and outcomes.

colorSV improves detection of structural variations in cancer research.

Research shows promise in personalizing cancer treatment using advanced analysis techniques.

SeNMo analyzes multi-omics data to improve cancer treatment and patient care.

WISER enhances cancer treatment predictions through innovative data techniques.

New insights into how immune therapy improves lung cancer outcomes.

New methods aim to improve mental health treatments by focusing on individual responses to medications.

Cardiovascular digital twins help tailor treatments by analyzing individual patient data.

New methods enhance discovery of predictive biomarkers from medical images.

Examining how algorithms can protect privacy while optimizing decisions in personalized medicine.

Precision nutrition uses gut health data for personalized dietary choices.

Research reveals gut health impacts Parkinson's disease progression and treatment.

A look into key mutations driving cancer progression and treatment options.

A new model enhances the analysis of complex health data.

A new approach improves treatment strategies by considering individual patient needs.

A new methodology improves treatment evaluation for personalized medicine.

ALISON models cancer behavior to tailor treatments for HGSOC patients.

HiFIT enhances predictions from high-dimensional omics data for better disease understanding.

GV-Rep aims to improve the analysis of genetic variants for patient care.

New methods improve drug response predictions for better cancer treatment options.

Matching-based policy learning improves treatment assignment based on individual characteristics.

Analyzing how individual traits affect medication responses through statistical methods.

Tiling method improves analysis of genomic data in Alzheimer's disease studies.

New models enhance predictions of gene function using individual genomic data.

DeepVul connects gene data to predict drug responses in cancer patients.

A look into how immune checkpoint blockade is changing melanoma care.

A new method enhances understanding of cancer drug responses through clearer explanations.

Research improves prediction models for Type 2 Diabetes risk using genetic data.

Exploring advancements and challenges in medical digital twin technology for personalized treatment.

Enhancing usability and reproducibility of drug response prediction in cancer research.

Exploring allele-specific expression and its role in cancer progression and treatment.

XPRS clarifies how genes influence polygenic risk scores for improved healthcare insights.

A new method improves predictions for individualized treatment effectiveness.

Research reveals genetic links to metabolites, aiding in health and disease understanding.