AIMC Topic: Machine Learning

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Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.

Pituitary Jun 1, 2020
PURPOSE: To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of...
Magnetic Resonance Imaging Pituitary Neoplasms Retrospective Studies Animals Humans Machine Learning
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Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Neuroinformatics Jun 1, 2020
Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are curren...
Signal Processing, Computer-Assisted Magnetoencephalography Machine Learning Brain Brain Mapping Electroencephalography Cross-Sectional Studies Humans
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Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.

Journal of digital imaging Jun 1, 2020
Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis...
Humans Neural Networks, Computer Machine Learning Breast Neoplasms Deep Learning Female
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Deep Learning and Transfer Learning for Optic Disc Laterality Detection: Implications for Machine Learning in Neuro-Ophthalmology.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society Jun 1, 2020
BACKGROUND: Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to deter...
Humans Machine Learning Algorithms Diagnostic Techniques, Ophthalmological Neurology Ophthalmology Optic Disk Optic Nerve Diseases Deep Learning ROC Curve
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Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.

Molecular imaging and biology Jun 1, 2020
PURPOSE: To identify optimal machine learning methods for radiomics-based differentiation of local recurrence versus inflammation from post-treatment nasopharyngeal positron emission tomography/X-ray computed tomography (PET/CT) images.
Male ROC Curve Image Processing, Computer-Assisted Retrospective Studies Female Neoplasm Recurrence, Local Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Diagnosis, Differential Positron Emission Tomography Computed Tomography Inflammation Machine Learning Humans Algorithms
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Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Integrative biology : quantitative biosciences from nano to macro May 21, 2020
Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochem...
Genes, p53 Machine Learning Outcome Assessment, Health Care Normal Distribution Algorithms PTEN Phosphohydrolase Probability Multivariate Analysis ROC Curve Tumor Suppressor Protein p53 Humans Decision Making Reproducibility of Results Signal Transduction DNA Damage
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Machine learning predicts new anti-CRISPR proteins.

Nucleic acids research May 21, 2020
The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that...
CRISPR-Associated Protein 9 Prophages Bacterial Proteins Streptococcus Sequence Analysis, Protein Machine Learning Proteome
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iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Briefings in bioinformatics May 21, 2020
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficie...
Machine Learning Algorithms Internet RNA Proteins Sequence Analysis DNA
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Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes.

Briefings in bioinformatics May 21, 2020
Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichme...
Gene Expression Profiling Computational Biology Machine Learning Algorithms
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Machine Learning Applications in Orthopaedic Imaging.

The Journal of the American Academy of Orthopaedic Surgeons May 15, 2020
Orthopedics Deep Learning Diagnostic Imaging Artificial Intelligence Machine Learning Humans
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