Abzu AI in transportation

Tag: QLattice.

Abzu’s QLattice® is an explainable AI that rationally reasons and makes evidence-based decisions.

Here we use the QLattice® to generate siRNA activity models from publicly available data to create insights that can be used to design active siRNAs.
Part one of a two-part developer diary on building a stratified data splitter for Abzu's python library Feyn and the QLattice.
Part one of a two-part developer diary on building a stratified data splitter for Abzu's python library Feyn and the QLattice.
Lykke Pedersen, Chief Pharma Officer at Abzu, considers the benefits of explainable AI and its applications within the pharma industry.
MYC targeting by OMO-103 in solid tumors: a phase 1 trial.
Dr. Andree Bates and Casper Wilstrup discuss how Abzu’s QLattice explainable AI algorithm can be used to accelerate disease understanding, drug design, and insights in Pharma R&D.
Lyt til Casper Wilstrup, CEO i Abzu, der ser, at next step er, at AI går fra et hjælpemiddel til direkte at overtage det menneskelige arbejde.
A discussion on AI, science, and philosophy, and Casper explains the symbolic AI behind Abzu's proprietary QLattice®.
Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice.
Casper Wilstrup explains the symbolic AI behind Abzu's proprietary QLattice®: An AI that generates explanations along with predictions.
An interpretable machine learning model using the gut microbiome to predict clinical E. faecium infection in human stem-cell transplant recipients.
Oligonucleotides Therapeutics Society (OTS) 2023 poster and keynote.
An Explainable Framework to Predict Child Sexual Abuse Awareness in People Using Supervised Machine Learning Models.
Using LLM Models and Explainable ML to Analyse Biomarkers at Single Cell Level for Improved Understanding of Diseases.
Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach.
A novel QLattice-based whitening machine learning model of landslide susceptibility mapping.
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.
Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers.
Information fusion via symbolic regression: A tutorial in the context of human health.
Part 2 on Deep Tech Stories: Casper Wilstrup on self-management and building a transparent organization.
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.
On Deep Tech stories podcast: An interview with Casper Wilstrup on he path to inventing a new kind of AI.
Quantum lattices for early cancer detection through machine learning.
On Forward: An interview with Casper Wilstrup for an in-depth intro into Abzu and Abzu's proprietary trustworthy AI technology.
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.
The simplest way to think about binary classification is that it is about sorting things into exactly two buckets.
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 development: Featurization and modeling 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.
Women with Turner Syndrome are both estrogen and androgen deficient: The impact of hormone replacement therapy.
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.
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.
Symbolic regression outperforms other models for small data sets.
An approach to symbolic regression using Feyn.
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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