AIMC Topic:
Supervised Machine Learning

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Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning.

IEEE transactions on medical imaging Jul 25, 2018
Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise ...
Humans Supervised Machine Learning Algorithms Image Processing, Computer-Assisted Hippocampus Magnetic Resonance Imaging White Matter
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Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis.

IEEE transactions on medical imaging Jul 24, 2018
We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated data sets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic...
Phantoms, Imaging Retina Supervised Machine Learning Optical Imaging Humans Databases, Factual Adult Middle Aged Image Processing, Computer-Assisted Fundus Oculi
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A Supervised Ensemble Approach for Sensitive microRNA Target Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Jul 20, 2018
MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target in...
Gene Expression Regulation Computational Biology RNA, Messenger Sequence Analysis, RNA MicroRNAs Supervised Machine Learning HEK293 Cells Humans Algorithms
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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports Jul 13, 2018
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...
Female Young Adult Adult Humans Databases, Factual Models, Biological Supervised Machine Learning Datasets as Topic Male ROC Curve Data Interpretation, Statistical Polysomnography Sleep, REM Sleep Latency Stochastic Processes Rare Diseases Narcolepsy
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Predicting Athlete Ground Reaction Forces and Moments From Spatio-Temporal Driven CNN Models.

IEEE transactions on bio-medical engineering Jul 9, 2018
The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded...
Sports Supervised Machine Learning Humans Athletes Neural Networks, Computer Pattern Recognition, Automated Female Biomechanical Phenomena Male Image Processing, Computer-Assisted
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Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.

Sensors (Basel, Switzerland) Jul 9, 2018
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, espec...
Humans Neural Networks, Computer Acceleration Automation Human Activities Smartphone Support Vector Machine Supervised Machine Learning
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A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
Drug Interactions Drug Development Probability Software Algorithms Supervised Machine Learning Technology, Pharmaceutical Predictive Value of Tests Drug Design Drug-Related Side Effects and Adverse Reactions Computational Biology Pharmaceutical Preparations
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Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting.

BMC bioinformatics Jun 26, 2018
BACKGROUND: Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput peptide mass fingerprinting has been largely applied to...
Peptides Supervised Machine Learning Collagen Species Specificity DNA Fingerprinting Humans
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Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.

IEEE journal of biomedical and health informatics Jun 25, 2018
Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as ov...
Image Interpretation, Computer-Assisted Humans Algorithms Histological Techniques Neural Networks, Computer Stomach Neoplasms Supervised Machine Learning
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Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA (New York, N.Y.) Jun 25, 2018
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to qua...
Animals Gene Expression Profiling Sequence Analysis, RNA Organ Specificity Supervised Machine Learning Alternative Splicing Data Mining Humans RNA
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