Ovarian Cancer

Composite pathway-guided Cox modeling for interpretable survival prediction in breast cancer.

Accurate survival prediction in breast cancer is essential for patient risk stratification and personalized treatment planning. Although transcriptomic data offer valuable insights into tumor biology, existing predictive models often suffer from poor interpretability and limited integration of biological knowledge. Standard gene-level feature selection methods, such as variance filtering and regul...

Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification.

Multi-element alloy catalysts exhibit tunable electronic structures and remarkable thermal stability, making them promising materials for automotive exhaust purification. However, most data-driven explorations have emphasised fresh activity, overlooking the post-ageing durability that governs real-world performance. Here, we have developed a closed-loop high-throughput discovery framework that emp...

Homologous recombination deficiency in Ovarian cancer: The game-changer for first-line maintenance therapy.

Homologous recombination deficiency (HRD) plays a central role in the pathogenesis and therapeutic vulnerability of epithelial ovarian cancer (EOC), p...

Quantification of nitrite in beverages and pickled foods with a high-sensitivity amperometric biosensor enhanced by a multilayer perceptron neural network.

Accurate nitrite detection in beverages and pickled foods is crucial for food safety but remains challenging due to matrix complexity, particularly in...

Interpretable deep learning model for pediatric strangulated small bowel obstruction on CT: A multicenter study.

OBJECTIVE: To develop and validate a deep learning-based multi-instance learning (MIL) model that integrates CT imaging and clinical data to improve t...

Uncovering Clinically Relevant Breast Cancer Subtypes Biomarkers Using Integrative Bioinformatics and Machine Learning Approaches.

A precise diagnosis and customized treatment become more difficult by the genomic heterogeneity of breast cancer (BRCA). In order to examine gene expr...

Gene driven analytical learning model for accurate breast cancer diagnosis.

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient ...

DFT insights into the stability of single-metal-atoms on Mo-based o-MXenes driven by the ligand effect.

MXenes, a novel class of two-dimensional transition metal carbides, nitrides or carbonitrides, have emerged as promising supports for single-atom cata...

Single-cell transcriptomic analysis and machine learning identify ATAD3A as a key gene that stabilizes mitochondrial-endoplasmic reticulum membranes, promoting bladder cancer progression.

BACKGROUND: Mitochondria-associated endoplasmic reticulum membranes (MAM) play a critical regulatory role in cancer, yet their function in bladder can...

Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning.

Cisplatin resistance limits the effectiveness of platinum-based chemotherapy for lung adenocarcinoma, yet practical systemic diagnostics for cisplatin...

Fragment-based drug design coupled with AI/ML prediction enables identification of novel PARP-1 inhibitors against triple-negative breast cancer.

Triple-negative breast cancer occurs as a formidable challenge in cancer research due to the characteristically aggressive behaviour, poor prognosis, ...

Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer.

Selective multimodal deep learning for reliable breast cancer subtype classification from histopathology and genomic data.

Accurate classification of breast cancer subtypes is critical for personalized treatment planning and prognostic assessment. While histopathology ...

Detection of esophageal varices and prediction of hepatic decompensation in unresectable hepatocellular carcinoma using AI.

BACKGROUND & AIMS: In hepatocellular carcinoma (HCC) with cirrhosis, portal hypertension worsens outcomes. Esophagogastroduodenoscopy (EGD), the curre...

Spatial and multi-omics transcriptomic dissects platinum resistance in lung adenocarcinoma: a five-gene predictive model with tumor microenvironment dynamics.

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge...

Diagnostic-Mediated Interactive Superlattices Engineering for Threshold-Saltatory Visual Assessment of Tuberculous Meningitis.

Nonlinear dynamic monitoring is crucial for assessing l-tryptophan (l-Trp) dysregulation progression in tuberculous meningitis (TBM); yet remains chal...

STAR (stroma-tumor AI risk) assessment: association of AI-derived tumor-stroma proportion with patient survival provides added prognostic value beyond KELIM in epithelial ovarian cancer.

BACKGROUND: There remains a critical need for prognostic biomarkers of treatment response in epithelial ovarian cancer (EOC). The KELIM score, derived...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.

Aging-related transcriptional programs shape breast cancer progression, immune regulation, and therapeutic response. We integrated curated aging-assoc...

Uncertainty-Aware Prediction of 195Pt Chemical Shifts from Limited Data.

Platinum (Pt) complexes are highly relevant for medicinal chemistry and homogeneous catalysis. In the development of novel Pt-based chemotherapeutic a...