Leukemia

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospi...

Breaking Down the Hierarchy: A New Approach to Leukemia Classification

The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the class...

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models

Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the ques...

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on lar...

UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction

Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, includin...

Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data

The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for unders...

Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification

Recent advancements in foundation models have transformed computer vision, driving significant performance improvements across diverse domains, incl...

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA...

Constructing Cell-type Taxonomy by Optimal Transport with Relaxed Marginal Constraints

The rapid emergence of single-cell data has facilitated the study of many different biological conditions at the cellular level. Cluster analysis ha...

A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images

Rationale and Objectives: Early prediction of pathological complete response (pCR) can facilitate personalized treatment for breast cancer patients....

Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning

A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells ...

Hypergraph Representations of scRNA-seq Data for Improved Clustering with Random Walks

Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abund...

LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, exi...

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational d...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

DeepPGDB: A Novel Paradigm for AI-Guided Interactive Plant Genomic Database

DeepPGDB (https://www.deeppgdb.chat) is the first AI-driven plant genomics database designed to lower technical barriers in multi-omics research by en...

TU_MyCo-Vision: A Deep Learning Tool for Detection of Cell Morphologies in Fungal Microscopic Images

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observat...

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within ...

Explainable machine learning-assisted exploration of chromatin dynamics reveals chromosome-specific response to serum starvation

Chromatin is dynamic at all length scales, influencing chromatin-based processes, such as gene expression. Even large-scale reorganization of whole ch...

De novo single-cell biological analysis of drug resistance in human melanoma through a novel deep learning-powered approach

Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Although scRNA-seq captures gene expression at the...