41 citations · 139 across the 17 of their papers we have counts for
9 papers · 1 filter
Systematic Reconstruction of Disease Networks from Longitudinal Blood Data for Causal Discovery and Intervention Analysis
David Patrick Duys Montealegre, Alexander Fulton, Mahta Haghighat Ghahfarokhi +2
We explore the hyperparameters and introduce a methodological framework to convert disease patterns from time series data of blood test results into correlation graphs for causal h…
A High-Fidelity Neurosurgical Training Platform for Bimanual Procedures: A Feasibility Study
Houssem-Eddine Gueziri, Abicumaran Uthamacumaran, Widad Safih +6
Background. Bimanual psychomotor proficiency is fundamental to neurosurgical procedures, yet it remains difficult for trainees to acquire and for educators to objectively evaluate…
XGBoost-Powered Digital Twins Leverage Routine Blood Tests for Early Detection of Cancer and Cardiovascular Disease
Lo Kai Shun John, Riya Nagar, Abicumaran Uthamacumaran +1
Early detection of cancer and cardiovascular diseases is fundamental to improving patient outcomes and reducing healthcare expenditure. Current cancer screening programs are target…
Exhaustive Investigation of CBC-Derived Biomarker Ratios for Clinical Outcome Prediction: The RDW-to-MCHC Ratio as a Novel Mortality Predictor in Critical Care
Dmytro Leontiev, Abicumaran Uthamacumaran, Riya Nagar +1
Ratios of common biomarkers and blood analytes are well established for early detection and predictive purposes. Early risk stratification in critical care is often limited by the…
Similarity Analysis of Blood Count Reference Intervals Across Continents Reveals No Reproducible Population or Geography-Linked Structure and Supports Personalised Values
Kunlin Wu, Abicumaran Uthamacumaran, Hector Zenil
Blood reference intervals (RIs) underpin diagnostic interpretation and therapeutic monitoring worldwide. However, many widely used RI systems originate from limited historical coho…
Complexity-Informed Causal Modeling of Neurodevelopmental Trajectories in Pediatric High-Grade Gliomas: Divergences from Neural Stem Cell Signatures
Abicumaran Uthamacumaran, Hector Zenil
Pediatric high grade gliomas are lethal evolutionary disorders with stalled developmental trajectories and disrupted differentiation hierarchies. We integrate transcriptional and a…
Neurosymbolic Learning for Predicting Cell Fate Decisions from Longitudinal Single Cell Transcriptomics in Paediatric Acute Myeloid Leukemia
Abicumaran Uthamacumaran, Hector Zenil
Paediatric Acute Myeloid Leukemia is a complex adaptive ecosystem with high morbidity. Current trajectory inference algorithms struggle to predict causal dynamics in AML progressio…
AI-Driven Hybrid Ecological Model for Predicting Oncolytic Viral Therapy Dynamics
Abicumaran Uthamacumaran, Juri Kiyokawa, Hiroaki Wakimoto
Oncolytic viral therapy (OVT) is an emerging precision therapy for aggressive and recurrent cancers. However, its clinical efficacy is hindered by the complexity of tumor-virus-imm…
Deep Learning-based Feature Discovery for Decoding Phenotypic Plasticity in Pediatric High-Grade Gliomas Single-Cell Transcriptomics
Abicumaran Uthamacumaran
By use of complex network dynamics and graph-based machine learning, we identified critical determinants of lineage-specific plasticity across the single-cell transcriptomics of pe…