2 citations · 4 across the 15 of their papers we have counts for
10 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…
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…
Multi-omic Enriched Blood-Derived Digital Signatures Reveal Mechanistic and Confounding Disease Clusters for Differential Diagnosis
Bolin Liu, Abicumaran Uthamacumaran, Alexander Fulton +1
Understanding disease relationships through blood biomarkers offers a pathway toward data-driven taxonomy and precision medicine. In this study, we constructed a digital blood twin…
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…
Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis
Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões +4
Understanding and controlling the informational complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and mo…
Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem
Alberto Hernández-Espinosa, Felipe S. Abrahão, Olaf Witkowski +1
The AI alignment problem, which focusses on ensuring that artificial intelligence (AI), including AGI and ASI, systems act according to human values, presents profound challenges.…
Can Complexity and Uncomputability Explain Intelligence? SuperARC: A Test for Artificial Super Intelligence Based on Recursive Compression
Alberto Hernández-Espinosa, Luan Ozelim, Felipe S. Abrahão +1
We introduce an increasing-complexity, open-ended, and human-agnostic metric to evaluate foundational and frontier AI models in the context of Artificial General Intelligence (AGI)…