Leukemia

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains th...

Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks.

Small sample sizes in biomedical research often led to poor reproducibility and challenges in transl...

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our abili...

scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.

Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chro...

Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.

Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or st...

High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.

Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse follo...

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective t...

NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patien...

scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder

scRNA-seq data has the potential to provide new insights into cellular heterogeneity and data acqu...

Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues

With the rapid growth of digital information, personalized recommendation systems have become an i...

Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line-Specific Synergistic Drug Combinations

Cancer is the second leading cause of death, with chemotherapy as one of the primary forms of trea...

Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering

Combating money laundering has become increasingly complex with the rise of cybercrime and digital...

Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for unders...

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to u...

Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models

Leukemia, a severe form of blood cancer, claims thousands of lives each year. This study focuses o...

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatm...

Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches

Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a ...

[Applications of artificial intelligence for imaging-driven diagnosis and treatment of bone and soft tissue tumors].

Bone and soft tissue tumors occur in the musculoskeletal system, and malignant bone tumors of bone a...

Clinical Validation of a Real-Time Machine Learning-based System for the Detection of Acute Myeloid Leukemia by Flow Cytometry

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase ...

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of ba...

Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma.