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Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.
Journal of cellular and molecular medicine
Jul 1, 2024
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...
Unsupervised Machine Learning in Countermovement Jump and Isometric Mid-Thigh Pull Performance Produces Distinct Combat and Physical Fitness Clusters in Male and Female U.S. Marine Corps Recruits.
Military medicine
Jun 26, 2024
INTRODUCTION: Several challenges face the U.S. Marine Corps (USMC) and other services in their efforts to design recruit training to augment warfighter mobility and resilience in both male and female recruits as part of an integrated model. Strength ...
MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.
JCO clinical cancer informatics
Jun 1, 2024
PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innov...
A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.
Briefings in bioinformatics
May 23, 2024
Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting vario...
Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.
Bioinformatics (Oxford, England)
Mar 29, 2024
MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-...
spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.
GigaScience
Jan 2, 2024
BACKGROUND: Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly wh...
Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These m...
Deep learning based decision tree ensembles for incomplete medical datasets.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to ...
DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.
Bioinformatics (Oxford, England)
Oct 3, 2023
MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq)...
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.
Briefings in bioinformatics
Sep 20, 2023
Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering al...
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