AIMC Topic: Machine Learning

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Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect.

Studies in health technology and informatics Jan 1, 2016
The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive ...
Video Games Whole Body Imaging Male Diagnosis, Computer-Assisted Pattern Recognition, Automated Female Imaging, Three-Dimensional Autistic Disorder Reproducibility of Results Actigraphy Humans Sensitivity and Specificity Machine Learning Mental Disorders
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Introducing Machine Learning Concepts with WEKA.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2016
This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple da...
Software Algorithms Computational Biology Machine Learning Databases, Genetic Data Mining
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Classification of Human Pregnane X Receptor (hPXR) Activators and Non-Activators by Machine Learning Techniques: A Multifaceted Approach.

Combinatorial chemistry & high throughput screening Jan 1, 2016
The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and ...
Receptors, Steroid Drug Resistance, Multiple Pregnane X Receptor Pregnanes Humans Machine Learning Bayes Theorem Support Vector Machine Drug Interactions
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Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization.

Current Alzheimer research Jan 1, 2016
The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on...
Algorithms Humans Fluorodeoxyglucose F18 Databases, Factual Aged, 80 and over Female Mental Status Schedule Machine Learning Brain Male Aged Brain Mapping Middle Aged Alzheimer Disease Cognitive Dysfunction Positron-Emission Tomography Follow-Up Studies
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Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels.

Asian Pacific journal of cancer prevention : APJCP Jan 1, 2016
BACKGROUND: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malig...
Decision Trees Small Cell Lung Carcinoma Lung Neoplasms Adenocarcinoma Support Vector Machine Biomarkers, Tumor Gene Expression Profiling Case-Control Studies Humans Machine Learning Algorithms Databases, Factual Carcinoma, Squamous Cell
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An Evaluation on Different Machine Learning Algorithms for Classification and Prediction of Antifungal Peptides.

Medicinal chemistry (Shariqah (United Arab Emirates)) Jan 1, 2016
BACKGROUND: Fungi are an emerging threat in medicine and agriculture and current therapeutics have proved to be insufficient and toxic. This has led to an increased interest in peptide-based therapeutics, especially antifungal peptides (AFPs), being ...
Machine Learning Antifungal Agents Algorithms Peptides
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Using Deep Learning for Compound Selectivity Prediction.

Current computer-aided drug design Jan 1, 2016
Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict...
Drug Design Probability Machine Learning Algorithms Computer-Aided Design Neural Networks, Computer
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ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2016
In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigati...
Humans Computational Biology Models, Theoretical Transcription Factors Databases, Genetic Machine Learning Algorithms Reproducibility of Results Cell Line Oligonucleotide Array Sequence Analysis Chromatin Immunoprecipitation
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Use of machine learning approaches for novel drug discovery.

Expert opinion on drug discovery Jan 1, 2016
INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery met...
Molecular Docking Simulation Binding Sites Drug Design Drug Discovery Models, Biological Humans Support Vector Machine Ligands Neural Networks, Computer Decision Trees Machine Learning
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INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2016
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in...
Algorithms Energy Intake Mobile Applications Humans Machine Learning Energy Metabolism Computational Biology Diet, Reducing Diet Records Cluster Analysis Data Mining Weight Reduction Programs
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