41 citations · 139 across the 17 of their papers we have counts for
22 papers
Causal Information Exchange as a Necessary Condition for Adaptive Regulation: A Perturbational Framework for Life and Agency
Brian LuValle, Abicumaran Uthamacumaran, Hector Zenil
Information exchange can support living organisation without providing a sufficient criterion for life. We develop a perturbational framework that distinguishes causal transmission…
scCTM: Continuous Thought Machine Reconstruction of Glioma Cell Fate Dynamics Attractor Landscapes and Algorithmic Perturbation Signatures
Abicumaran Uthamacumaran
Cell fate plasticity dynamics in glioma ecosystems is usually inferred from static single-cell snapshots, leaving a gap between manifold geometry, predictive modelling, and mechani…
Reconstruction of Geometric and Structural Aspects of Undeciphered Messages by Information-theoretic Methods
Hector Zenil, Abicumaran Uthamacumaran
In previous work, we established sufficient conditions for recovering geometry and topology from non-random information, demonstrating reconstruction with the Arecibo message. Unde…
What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal
Luan Ozelim, Abicumaran Uthamacumaran, Hector Zenil
A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them closer to Solomonoff induction,…
Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI
Henri Feto, Abicumaran Uthamacumaran, Hector Zenil
Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We…
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…
Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms
Luan Ozelim, Abicumaran Uthamacumaran, Felipe S. Abrahão +4
Assembly Theory (AT) and its central measure, the assembly index (Ai), represent an invaluable opportunity to address some of the most persistent and widespread conflations and mis…
Integrative Adaptive Indexes from Noisy Routine Haematological Markers can Predict and Discriminate Health Status and Biological Age
Santiago Hernández-Orozco, Abicumaran Uthamacumaran, Francisco Hernández-Quiroz +2
For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor inter…
On the Salient Limitations of the Methods of Assembly Theory and their Classification of Molecular Biosignatures
Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani +1
We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all…
Machine Intelligence-Driven Classification of Cancer Patients-Derived Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results from a Pilot Study
Abicumaran Uthamacumaran, Mohamed Abdouh, Kinshuk Sengupta +5
Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid biopsy diagnostics to aid in early detection, cancer screening,…
A Review of Mathematical and Computational Methods in Cancer Dynamics
Abicumaran Uthamacumaran, Hector Zenil
Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory…
Machine Learning Characterization of Cancer Patients-Derived Extracellular Vesicles using Vibrational Spectroscopies
Abicumaran Uthamacumaran, Samir Elouatik, Mohamed Abdouh +3
The early detection of cancer is a challenging problem in medicine. The blood sera of cancer patients are enriched with heterogeneous secretory lipid bound extracellular vesicles (…
Pattern Detection on Glioblastoma's Waddington landscape via Generative Adversarial Networks
Abicumaran Uthamacumaran
Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis. Their emergent properties such as cellular heterogeneity, therapy resistance, and self-renewal are lar…
A Review of Complex Systems Approaches to Cancer Networks
Abicumaran Uthamacumaran
Cancers remain the lead cause of disease-related, pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system wi…