Tag: QLattice.
Abzu’s QLattice® is an explainable AI that rationally reasons and makes evidence-based decisions.
Plasma proteomics discovery of mental health risk biomarkers in adolescents.
Analysis of the relationship between fetal health prediction features with machine learning Feyn QLattice regression model.
Information fusion via symbolic regression: A tutorial in the context of human health.
Abzu® announced that the United States Patent and Trademark Office (USPTO) has issued Abzu Aps a patent number US 11,537,686 titled “Method of Deriving a Correlation” that protects the technology behind the pioneering QLattice algorithm.
Quantum lattices for early cancer detection through machine learning.
Abzu will be applying the €2.5 million grant to the development of the Abzu AI platform, a user-friendly and easy-to-operate platform built on the QLattice engine that makes interpretable and explainable predictions widely accessible.
Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach.
Identifying interactions in omics data for clinical biomarker discovery using symbolic regression.
Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths.
Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia.
Explainable “white-box” machine learning is the way forward in preeclampsia screening.
We're thrilled to announce that Gartner has named Abzu a "Cool Vendor" in AI for excelling in explainability, fairness, and trustworthiness.
An example of peptide drug featurization and modelling using anticancer peptides.
Let's study how well calibrated the QLattice models are, and to what extent calibrators can improve them.
What do machine learning model outputs represent? What if the predictions that we're making come with a future risk?
Calibrators are tools used to transform the scores generated by your models into (almost) real mathematical probabilities.
Explainable long-term building energy consumption prediction using the QLattice.
In under 2 mins: Why we have to understand what the decisions we make are based on and not blindly trust that a computer is right.
The QLattice, a new explainable AI algorithm, can cut through the noise of omics data sets and point to the most relevant inputs and models.
Our results-driven world pressures us to give up good science.
Curing your data preprocessing blues: Automatic handling of categorical data and scaling
“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.
Data sciencey-sphere, I have big news. A radical new machine learning model has surfaced.
Explainable AI with the QLattice and feynplots
Do you think about machine learning? How about all the research put into self-driving cars or image recognition or natural language processing?
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