AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes.

In silico perturbation models, computational methods that can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as $R^{2}$, which assess overall prediction accuracy but fail to capture biologically significant outcomes like the identification of differentially expressed (DE) genes.

AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models.

In silico perturbation models, computational methods which can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as , which assess overall prediction accuracy but fail to capture biologically significant outcomes like the identification of differentially expressed genes.

A longitudinal study of pupillary light reflex in 6- to 24-month children.

Pupillary light reflex (PLR) is an involuntary response where the pupil size changes with luminance. Studies have shown that PLR response was altered in children with autism spectrum disorders (ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied.

High resolution imaging of the fibrous microstructure in bovine common carotid artery using optical polarization tractography.

The biomechanical properties of artery are primarily determined by the fibrous structures in the vessel wall. Many vascular diseases are associated with alternations in the orientation and alignment of the fibrous structure in the arterial wall. Knowledge on the structural features of the artery wall is crucial to our understanding of the biology of vascular diseases and the development of novel therapies.

Optimizing dynamical similarity index extraction window for seizure detection.

This paper addresses an optimization problem in choosing optimum window length for feature extraction in automatic seizure detection. The processing window length plays an important role in reducing the false positive and false negative rates and decreasing required processing time for seizure detection. This study presents an approach for selecting the optimum window length toward the extraction of dynamical similarity index (DSI) feature.

Seizure prediction using adaptive neuro-fuzzy inference system.

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis.