Sahand Jamal Rahi

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The optimal strategy balancing risk and speed predicts DNA damage checkpoint override times

Cells have built-in mechanisms for checking for errors during replication, but these checkpoints can slow down reproduction. A theory accounting for optimal checkpoint strategies is presented and tested against observations in budding yeast. Checkpoints ar ...

2022

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Nahal Mansouri, Sahand Jamal Rahi

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

FRONTIERS MEDIA SA2022

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

Nahal Mansouri, Sahand Jamal Rahi

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation ...

FRONTIERS MEDIA SA2021

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

Nahal Mansouri, Sahand Jamal Rahi

EUROPEAN RESPIRATORY SOC JOURNALS LTD2021

Using Machine Learning to Predict mortality for first-day COVID-19 patients presenting to the ICU

Nahal Mansouri, Sahand Jamal Rahi

EUROPEAN RESPIRATORY SOC JOURNALS LTD2021

A convolutional neural network segments yeast microscopy images with high accuracy

Anne-Florence Raphaëlle Bitbol, Sahand Jamal Rahi, Vojislav Gligorovski, Mateusz Kozinski, Martin Weigert, Nicola Dietler, Matthias Minder, Ahmad Sadeghi

The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit i ...

Nature Research2020

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