AIMC Topic:
Supervised Machine Learning

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Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Jul 13, 2017
MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help dev...
Supervised Machine Learning Gene Expression Profiling Transcriptome Computational Biology Gene Regulatory Networks RNA, Messenger Support Vector Machine Sequence Analysis, RNA MicroRNAs Unsupervised Machine Learning
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Effects of additional data on Bayesian clustering.

Neural networks : the official journal of the International Neural Network Society Jul 12, 2017
Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional infor...
Cluster Analysis Models, Statistical Bayes Theorem Supervised Machine Learning
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Constrained Deep Weak Supervision for Histopathology Image Segmentation.

IEEE transactions on medical imaging Jul 7, 2017
In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS);...
Neural Networks, Computer Algorithms Databases, Factual Humans Supervised Machine Learning Histocytochemistry Colon Tissue Array Analysis Image Processing, Computer-Assisted Colonic Neoplasms
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ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

Genome medicine Jun 29, 2017
BACKGROUND: A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same do...
High-Throughput Nucleotide Sequencing DNA Mutational Analysis Mutation Supervised Machine Learning Humans Neoplasms
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Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.

IEEE journal of biomedical and health informatics Jun 8, 2017
Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individua...
Spirometry Supervised Machine Learning Quality Control Aged Adult Middle Aged ROC Curve Decision Support Systems, Clinical Signal Processing, Computer-Assisted Humans
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A review of active learning approaches to experimental design for uncovering biological networks.

PLoS computational biology Jun 1, 2017
Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such ...
Algorithms Research Design Supervised Machine Learning Gene Regulatory Networks Computational Biology Metabolic Networks and Pathways
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Classification of Paediatric Inflammatory Bowel Disease using Machine Learning.

Scientific reports May 25, 2017
Paediatric inflammatory bowel disease (PIBD), comprising Crohn's disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PI...
Unsupervised Machine Learning Supervised Machine Learning Machine Learning Models, Theoretical Age Factors Infant Humans Child Inflammatory Bowel Diseases Cluster Analysis Adolescent Infant, Newborn ROC Curve Female Child, Preschool Reproducibility of Results Male
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An inference method from multi-layered structure of biomedical data.

BMC medical informatics and decision making May 18, 2017
BACKGROUND: Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of om...
Humans Algorithms Comorbidity Computational Biology Biological Science Disciplines Supervised Machine Learning Diagnosis
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Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning.

Medical image analysis May 13, 2017
Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is base...
Sensitivity and Specificity Alzheimer Disease Female Disease Progression Humans Reproducibility of Results Male Parkinson Disease Machine Learning Aged Supervised Machine Learning
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3D deeply supervised network for automated segmentation of volumetric medical images.

Medical image analysis May 8, 2017
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affect...
Reproducibility of Results Sensitivity and Specificity Imaging, Three-Dimensional Supervised Machine Learning Humans Neural Networks, Computer
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