Oncology/Hematology

Immune Checkpoint Activity and Prognostic Roles of TNFRSF8, CD160, and TNFSF9 in Primary and Brain Metastatic NSCLC.

Here, we utilized advanced bioinformatics approaches alongside experimental validation to identify key prognostic biomarkers and potential immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). Our findings highlight distinct differences in the immune and molecular environments between primary and brain metastatic NSCLC. Brain metastases exhibited higher proportions of cancer s...

ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and machine learning.

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions, yet CF-MS data generation is of low throughput and therefore predictive models are often limited by the scarcity and limited diversity of high-quality training data. To address this, we present ProteoAutoNet, a robotic experimental platform integrated with a computational workflow f...

A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.

Accurate prediction of recurrence risk after hepatectomy still remains a clinical challenge for hepatocellular carcinoma (HCC). We develop a deep lear...

Protein buffering of aneuploidy is driven by coordinated factors identified through machine learning.

Aneuploidy, a hallmark of cancer, alters chromosome copy numbers and with that the abundance of hundreds of proteins. Evidence suggests that levels of...

Integrative multi-omics and machine learning framework identifies PRDX4 as a redox-EMT regulator and predictive marker in bone-metastatic breast cancer.

Bone metastasis is a major cause of morbidity and mortality in breast cancer, yet effective prognostic models and targeted therapies remain limited. H...

Integrated network toxicology, machine learning, and bioinformatics analysis reveals sodium dehydroacetate-induced coagulation dysfunction in colorectal cancer.

Coagulation dysfunction, a common hematologic disorder with unclear pathogenesis, is influenced by environmental factors. Sodium dehydroacetate (SDA),...

Review of flow cytometry findings and associated scoring approaches for identifying myelodysplastic syndrome and the future role of machine learning in improving the diagnostic algorithm.

OBJECTIVE: Myelodysplastic syndrome (MDS) is a heterogeneous set of neoplasms that require careful exclusion of potential mimics before diagnosis. In ...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.

Canine mammary tumors (CMTs) are the most common neoplasms in intact female dogs, yet early detection remains challenging due to the lack of clinicall...

Artificial intelligence and machine learning driven nanorobotics for targeted brain delivery: Redefining blood-brain barrier navigation.

The blood-brain barrier (BBB) plays a central role in preserving central nervous system (CNS) homeostasis, and its dysfunction is implicated in variou...

ClathPLM: Deep multi-view feature extraction with CNN and attention enhances clathrin protein identification.

Clathrin is a key structural protein in intracellular vesicle transport, mainly mediating clathrin-mediated endocytosis (CME) through trimeric assembl...

Integrating thermal liquid biopsy, clinical data, and mass spectrometry for early diagnosis and biomarker discovery in colorectal cancer.

Early detection of colorectal cancer is essential to improving survival, where yet current diagnostic tools show limited performance. This study aimed...

HybridDeepSynergy: A hybrid deep learning model integrating CNN, LSTM, and attention mechanisms for cancer drug synergy prediction.

A variety of AI-based approaches have been employed to analyze complex genomic datasets. Predicting the synergy of drug combinations is a critical ste...

Artificial Intelligence Applications in Thyroid Cancer Care.

CONTEXT: Artificial intelligence (AI) has created tremendous opportunities to improve thyroid cancer care. EVIDENCE ACQUISITION: We used the "artifici...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds p...

Immune Predictors of Radiotherapy Outcomes in Cervical Cancer.

The immune microenvironment influences the sensitivity of patients to radiotherapy (RT), yet determinants of therapeutic resistance remain elusive. Th...

Artificial Intelligence Helps Diagnose Oral Potentially Malignant Disorders: A Systematic Review and Meta-Analysis.

INTRODUCTION: Oral potentially malignant disorders (OPMDs) can lead to oral cancer, which is one of the most common cancers worldwide. Prevention is c...

Next-generation cancer therapies: translating experimental advances into clinical practice.

Recent advancements in cancer therapy have led to the emergence of innovative approaches that offer new hope to patients. This review provides a compr...