Maria Anna Rapsomaniki
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View article: Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML
Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML Open
Most pediatric acute myeloid leukemia (pAML) patients achieve complete remission after chemotherapy, yet relapse is common, with nearly 40% ultimately dying of the disease. Prognosis is currently assessed using cytogenetic biomarkers and m…
View article: Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap
Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap Open
Learning the response of single-cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport (OT) has emerged as a principled methodological framework because it inherently acco…
View article: Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport
Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport Open
Chimeric Antigen Receptor (CAR) T cell therapy is a promising area of cancer immunotherapy. However, many challenges such as loss of persistence, T cell exhaustion, and therapy associated toxicities hamper further advancement of CAR T cell…
View article: Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling
Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling Open
View article: Navigating the immunosuppressive brain tumor microenvironment using spatial biology
Navigating the immunosuppressive brain tumor microenvironment using spatial biology Open
With the application of spatial biology, the detection and identification of the diverse cell types present in the tumor microenvironment, including specific immune subsets, is possible at single cell resolution. Since spatial biology anal…
View article: Overcoming limitations in current measures of drug response may enable AI-driven precision oncology
Overcoming limitations in current measures of drug response may enable AI-driven precision oncology Open
Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of person…
View article: ScLinear predicts protein abundance at single-cell resolution
ScLinear predicts protein abundance at single-cell resolution Open
Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. …
View article: Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions
Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions Open
Understanding the spatial heterogeneity of tumors and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin & eosin (H&E) and serial immunohistoc…
View article: Overcoming limitations in current measures of drug response to enable AI-driven precision oncology
Overcoming limitations in current measures of drug response to enable AI-driven precision oncology Open
Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we unveil fundamental issues with the use of standard measures of drug sensitivity that hinder the development of pe…
View article: Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions
Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions Open
Understanding the spatial heterogeneity of tumors and its links to disease is a cornerstone of cancer biology. Emerging spatial technologies offer unprecedented capabilities towards this goal, but several limitations hinder their clinical …
View article: Hybrid quantum-classical graph neural networks for tumor classification in digital pathology
Hybrid quantum-classical graph neural networks for tumor classification in digital pathology Open
Advances in classical machine learning and single-cell technologies have paved the way to understand interactions between disease cells and tumor microenvironments to accelerate therapeutic discovery. However, challenges in these machine l…
View article: Towards quantum-enabled cell-centric therapeutics
Towards quantum-enabled cell-centric therapeutics Open
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future quantum computers will be capable of performing simulations fo…
View article: Matching single cells across modalities with contrastive learning and optimal transport
Matching single cells across modalities with contrastive learning and optimal transport Open
Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecul…
View article: ChromFormer: A transformer-based model for 3D genome structure prediction
ChromFormer: A transformer-based model for 3D genome structure prediction Open
Recent research has shown that the three-dimensional (3D) genome structure is strongly linked to cell function. Modeling the 3D genome structure can not only elucidate vital biological processes, but also reveal structural disruptions link…
View article: MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport
MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport Open
Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. However, the resulting multimo…
View article: scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data
scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data Open
With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identi…
View article: ATHENA: analysis of tumor heterogeneity from spatial omics measurements
ATHENA: analysis of tumor heterogeneity from spatial omics measurements Open
Summary Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources tha…
View article: Quantification of tumor heterogeneity: from data acquisition to metric generation
Quantification of tumor heterogeneity: from data acquisition to metric generation Open
View article: Modeling the Three-Dimensional Chromatin Structure from Hi-C Data with Transfer Learning
Modeling the Three-Dimensional Chromatin Structure from Hi-C Data with Transfer Learning Open
Recent studies have revealed the importance of three-dimensional (3D) chromatin structure in the regulation of vital biological processes. Contrary to protein folding, no experimental procedure that can directly determine ground-truth 3D c…
View article: Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity
Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity Open
A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, es…
View article: <i>In silico</i> analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity
<i>In silico</i> analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity Open
DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion ac…
View article: Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity
Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity Open
A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, es…
View article: <i>In silico</i>analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity
<i>In silico</i>analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity Open
DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion ac…
View article: A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer Open
View article: A stochastic hybrid model of DNA replication incorporating 3D protein mobility dynamics
A stochastic hybrid model of DNA replication incorporating 3D protein mobility dynamics Open
Summary DNA replication, the basis of genetic information maintenance, is a remarkably robust yet highly stochastic process. We present a computational model that incorporates experimental genome structures and protein mobility dynamics to…
View article: Inference of the three-dimensional chromatin structure and its temporal behavior
Inference of the three-dimensional chromatin structure and its temporal behavior Open
Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the genome folds within the nucleus, chromosome conformation captur…
View article: EasyFRAP-web: a web-based tool for the analysis of fluorescence recovery after photobleaching data
EasyFRAP-web: a web-based tool for the analysis of fluorescence recovery after photobleaching data Open
Understanding protein dynamics is crucial in order to elucidate protein function and interactions. Advances in modern microscopy facilitate the exploration of the mobility of fluorescently tagged proteins within living cells. Fluorescence …
View article: CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
CellCycleTRACER accounts for cell cycle and volume in mass cytometry data Open
View article: IPP Complex Reinforces Adhesion by Relaying Tension-Dependent Signals to Inhibit Integrin Turnover
IPP Complex Reinforces Adhesion by Relaying Tension-Dependent Signals to Inhibit Integrin Turnover Open
View article: IPP Complex Reinforces Adhesion by Relaying Tension-Dependent Signals to Inhibit Integrin Turnover
IPP Complex Reinforces Adhesion by Relaying Tension-Dependent Signals to Inhibit Integrin Turnover Open