AIMC Topic: Machine Learning

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Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner.

BMC medical genomics
BACKGROUND: Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is requi...

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

The New phytologist
Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pat...

Logic regression-derived algorithms for syndromic management of vaginal infections.

BMC medical informatics and decision making
BACKGROUND: Syndromic management of vaginal infections is known to have poor diagnostic accuracy. Logic regression is a machine-learning procedure which allows for the identification of combinations of variables to predict an outcome, such as the pre...

Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders.

NeuroImage
Classification models are becoming useful tools for finding patterns in neuroimaging data sets that are not observable to the naked eye. Many of these models are applied to discriminating clinical groups such as schizophrenic patients from healthy co...

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients.

NeuroImage
Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with ...

Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques.

NeuroImage
First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for exampl...

Decoding intracranial EEG data with multiple kernel learning method.

Journal of neuroscience methods
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their app...

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

Journal of medical systems
Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were inves...

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching.

IEEE transactions on medical imaging
Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogen...

Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.

Methods (San Diego, Calif.)
Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maint...