Leukemia

Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.

X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development.

In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This paper introduces an explainable PCA-based deep learning attention autoencoder model, X-scPAE (eXplained Single Cell PCA - Attention Auto Encoder), wh...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemo...

Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis.

Human and animal health rely on balancing cell division and cell death to maintain normal homeostasis. This process is accomplished by regulated cell ...

Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-inva...

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA su...

Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.

BACKGROUND: Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. Th...

Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.

Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potenti...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using gen...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging ou...

Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology.

The surface modification of titanium (Ti) and its alloys is crucial for improving their osteogenic capability, as their bio-inert nature limits effect...

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiom...

Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.

Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularl...

Unveiling genetic basis of seedling emergence from deep soil depth under dry direct- seeded conditions in rice ( L.).

Water scarcity and labor shortage pose significant challenges in rice farming. Direct-seeded rice (DSR) is an efficient method that conserves water, r...

F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer...

Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically presents with poor prognosis. Lactate is a crucial me...

Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of p...

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effect...

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

BACKGROUND: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of inf...

Hyoid bone-based sex discrimination among Egyptians using a multidetector computed tomography: discriminant function analysis, meta-analysis, and artificial intelligence-assisted study.

The hyoid bone has been identified as sexually dimorphic in various populations. The current study is a forerunner analysis that used three-dimensiona...