Hematology

Latest AI and machine learning research in hematology for healthcare professionals.

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Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. We performed comprehensive RNA-sequencing of skin an...

CanID: a robust and accurate RNAseq Expression-based diagnostic classification scheme for pediatric malignancies

Cancer subtype classification is critical for precision therapy and there is a growing trend of augmenting histopathology testing procedures with omics-based machine learning classifiers. However, analytical challenges remain for pediatric cancer on the scope and precision of the current classifiers as well as the evolving subtype standardization. To address these challenges, we built Cancer Ident...

Machine learning assisted classification of cell and brain penetrating peptides

Crossing the blood–brain barrier (BBB) remains a major obstacle for central nervous system therapeutics. Short peptides have emerged as promising vect...

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment ...

Leaping over the blood-brain barrier: DNA methylation as a link between peripheral and central immune systems

The majority of existing DNA methylation (DNAm) studies have used peripheral surrogate tissues to research molecular mechanisms underlying brain-relat...

Single Capture Quantitative Oblique Back-Illumination Microscopy

Quantitative oblique back-illumination microscopy (qOBM) has emerged as a powerful technique for label-free, 3D quantitative phase imaging of arbitrar...

ChatMDV: Democratising Bioinformatics Analysis Using Large Language Models

The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable ...

A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy

Functional hyperaemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the ...

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

Extracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo refle...

Accurate and robust classification of Mycobacterium bovis-infected cattle using peripheral blood RNA-seq data

The zoonotic bacterium, Mycobacterium bovis, causes bovine tuberculosis (bTB) and is closely related to Mycobacterium tuberculosis, the primary cause ...

ppIRIS: deep learning for proteome-wide prediction of bacterial protein-protein interactions

Protein–protein interactions (PPIs) are central to cellular processes and host–pathogen dynamics, yet bacterial interactomes remain poorly mapped, esp...

Advanced Deep Learning Enables Prediction of Allogeneic Stem Cell Mobilization Success

Hematopoietic stem and progenitor cell (HSPC) transplantation offers a potentially curative therapy for aggressive hematologic malignancies and bone m...

Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits

All cells in an animal collectively ensure, moment-to-moment, the survival of the whole organism in the face of environmental stressors1,2. Physiology...

Revive-Flow: A Foundation Model for Blood DNAm Aging

Epigenetic clocks can predict biological age but cannot prescribe the interventions needed to reverse it. Here, we introduce REjuVenatIon Via Epigenet...

Deep Learning Detection and Classification of Red Blood Cells: Towards a Universal Dataset

We evaluate emerging machine learning models for pattern recognition, focusing on the YOLOv11 architecture for detecting and classifying red blood cel...

Predicting Risk of Transfusion-Induced Red Blood Cell Alloimmunization Using Statistical and Machine Learning Approaches in the Recipient Epidemiology and Donor Evaluation Study (REDS-III) Database

Red blood cell (RBC) alloimmunization is a common complication from blood transfusion, often resulting in accelerated donor RBC destruction. Patients ...

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate ...

New proteomic biomarkers identified in plasma extracellular vesicles in sarcoidosis: a case-control matched study

Sarcoidosis is a heterogeneous disease with unknown mechanisms, nonspecific therapies, and multiple etiologies. The role of blood extracellular vesicl...

Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells

In hematology/oncology clinics, molecular diagnostics based on nucleic acid sequencing or hybridization are routinely employed to detect malignancy-as...

System-level health profiling from blood DNA methylation with explainable deep learning

Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability...

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