AIMC Topic: Machine Learning

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An ensemble approach to accurately detect somatic mutations using SomaticSeq.

Genome biology
SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currentl...

Compositional Dictionaries for Domain Adaptive Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We present a dictionary learning approach to compensate for the transformation of faces due to the changes in view point, illumination, resolution, and so on. The key idea of our approach is to force domain-invariant sparse coding, i.e., designing a ...

Horror Image Recognition Based on Context-Aware Multi-Instance Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme e...

Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.

Journal of biomedical informatics
Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) ...

A study of active learning methods for named entity recognition in clinical text.

Journal of biomedical informatics
OBJECTIVES: Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they oft...

Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

BMC genomics
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hund...

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

International journal of neural systems
Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the ...

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...

Exploiting Language Models to Classify Events from Twitter.

Computational intelligence and neuroscience
Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguis...

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.

Computational and mathematical methods in medicine
The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the ...