AIMC Topic: Machine Learning

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Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.

Physical biology
Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free q...

Intracortical neural activity distal to seizure-onset-areas predicts human focal seizures.

PloS one
The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Here, we present initial results and a proof-of-concept of how focal seizures can be predicted early in...

A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

Biotechnology and bioengineering
The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiq...

From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder.

Neuroscience and biobehavioral reviews
Pattern classification and stratification approaches have increasingly been used in research on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation towards clinical applicability. Here, we present an extensive scoping ...

Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning.

Molecular informatics
In this paper we used two sets of calculated molecular descriptors to predict blood-brain barrier (BBB) entry of a collection of 415 chemicals. The set of 579 descriptors were calculated by Schrodinger and TopoCluj software. Polly and Triplet softwar...

Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.

Journal of medical systems
Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best m...

Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research.

Journal of medicinal chemistry
Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for no...

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

British journal of anaesthesia
BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine le...