Leukemia

Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder

Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combining morphological and transcriptomic data, we can gain insights into cellular functions and trajectoriess involved in blood cell differentiation. However, existing methodologies struggle with integrating morphological and transcriptomic data, leaving a...

scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clustering methods. To address these issues, we propose a deep clustering method, Attention-Enhanced Structural Deep Embedding Graph Clustering (scASDC), which inte...

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC...

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring ...

scGHSOM: Hierarchical clustering and visualization of single-cell and CRISPR data using growing hierarchical SOM

High-dimensional single-cell data poses significant challenges in identifying underlying biological patterns due to the complexity and heterogeneity...

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make th...

Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microsc...

Can virtual staining for high-throughput screening generalize?

The large volume and variety of imaging data from high-throughput screening (HTS) in the pharmaceutical industry present an excellent resource for t...

PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro with In-Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout

Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memo...

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolutio...

Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks.

This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodologies for the analysis of cough patterns in respira...

Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers

Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this maligna...

MMIL: A novel algorithm for disease associated cell type discovery

Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introdu...

Countrywide natural experiment reveals impact of built environment on physical activity

While physical activity is critical to human health, most people do not meet recommended guidelines. More walkable built environments have the poten...

Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, ...

A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods

Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow...

How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses.

Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neu...

Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.

BACKGROUND: Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cell...

A Machine Learning Model to Predict the Histology of Retroperitoneal Lymph Node Dissection Specimens.

BACKGROUND/AIM: While post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) benefits patients with teratoma or viable germ cell tumors (G...

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels.

The demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discer...