AIMC Topic: Machine Learning

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Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

Journal of theoretical biology Mar 27, 2017
Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid ...
Sequence Analysis, Protein Protein Folding Sequence Homology, Amino Acid Machine Learning Computational Biology Principal Component Analysis Amino Acid Sequence Datasets as Topic Protein Conformation
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ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.

PloS one Mar 27, 2017
High prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. For computational biology, positi...
Computational Biology Machine Learning Support Vector Machine Algorithms
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Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia Mar 24, 2017
BACKGROUND AND AIM: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.
Humans Machine Learning Male Diagnosis, Computer-Assisted Cognitive Dysfunction Female Aged Aged, 80 and over Dementia, Vascular Diagnosis, Differential Alzheimer Disease
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Toward a systematic exploration of nano-bio interactions.

Toxicology and applied pharmacology Mar 24, 2017
Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships bet...
Nanotechnology Machine Learning Algorithms Toxicity Tests Animals Animal Testing Alternatives Risk Assessment Computational Biology Nanostructures Humans Artificial Intelligence
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Gland Instance Segmentation Using Deep Multichannel Neural Networks.

IEEE transactions on bio-medical engineering Mar 23, 2017
OBJECTIVE: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also b...
Image Processing, Computer-Assisted Intestinal Mucosa Humans Histocytochemistry Neural Networks, Computer Colorectal Neoplasms Machine Learning Colon Algorithms
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Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society Mar 23, 2017
UNLABELLED: The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were select...
Humans Machine Learning Algorithms Regression Analysis Osteoarthritis, Knee Cartilage, Articular Middle Aged Cohort Studies Magnetic Resonance Imaging
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Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Psychiatry research. Neuroimaging Mar 23, 2017
Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures ...
Young Adult Adult Depressive Disorder, Major Humans Brain Machine Learning Female Algorithms White Matter Diffusion Tensor Imaging Adolescent Support Vector Machine Brain Mapping
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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics Mar 23, 2017
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...
Humans Data Accuracy Protein Conformation Models, Statistical Machine Learning Proteins Animals Models, Molecular Protein Binding
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Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

Computers in biology and medicine Mar 22, 2017
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) arch...
Humans Carcinoma, Hepatocellular Liver Histocytochemistry Neoplasm Grading Algorithms Biopsy, Needle Neural Networks, Computer Diagnosis, Computer-Assisted Machine Learning Liver Neoplasms
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Evaluating deep learning architectures for Speech Emotion Recognition.

Neural networks : the official journal of the International Neural Network Society Mar 21, 2017
Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER th...
Machine Learning Emotions Speech Recognition Software Neural Networks, Computer
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