Leukemia

Exploring an novel diagnostic gene of trastuzumab-induced cardiotoxicity based on bioinformatics and machine learning.

Trastuzumab (Tra)-induced cardiotoxicity (TIC) is a serious side effect of cancer chemotherapy, which can seriously harm the health of cancer patients. However, there is currently a lack of effective and reliable biomarkers for the early diagnosis of TIC in clinical practice. Therefore, we screened the TIC candidate diagnostic gene solute carrier family 6 member 6 (SLC6A6) by combining multi-machi...

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.

BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data.

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study.

Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine...

Machine learning methods to identify risk factors for corneal graft rejection in keratoconus.

Machine learning can be used to identify risk factors associated with graft rejection after corneal transplantation for keratoconus. The study include...

Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1-weighted MRI generation from radiographs.

This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long-term glucocorticoid ther...

Differential impact of CD34+ cell dose for different age groups in allogeneic hematopoietic cell transplantation for acute leukemia: a machine learning-based discovery.

Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies yet carries associated...

Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.

Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent mar...

Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete respons...

Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.

BACKGROUND: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematop...

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making t...

Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients.

The objective of this study was to develop a predictive model using supervised machine learning to determine sex based on the dimensions of the hyoid ...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical...

Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).

A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cypr...

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Many studies have shown that infectious diseases are responsible for the majority of deaths in children under five. Among these children, Acute Respir...

Generalizable self-supervised learning for brain CTA in acute stroke.

Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks ...

Using bioinformatics and artificial intelligence to map the cyclin-dependent kinase 4/6 inhibitor biomarker landscape in breast cancer.

A cyclin-dependent kinase 4/6 (CDK4/6) inhibitor combined with endocrine therapy is the standard-of-care for patients with hormone receptor-positive/h...

SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer.

Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing bet...

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of brea...

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome.

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about signific...

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse.

Interferon-gamma (IFN-γ) is a versatile pleiotropic cytokine essential for both innate and adaptive immune responses. It exhibits both pro-inflammator...