AIMC Topic: Machine Learning

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AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research Jun 4, 2019
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...
Machine Learning Algorithms Reproducibility of Results Humans Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Binding Sites Chromatin Immunoprecipitation Protein Binding Computational Biology Transcription Factors
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Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence.

Investigative ophthalmology & visual science Jun 3, 2019
PURPOSE: To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for funct...
Antigens, Neoplasm Neoplasm Proteins Tomography, Optical Coherence Mutation Visual Fields Visual Field Tests Male Middle Aged Adolescent Female Young Adult Adult Leber Congenital Amaurosis Retinitis Pigmentosa Color Vision Defects Retinal Cone Photoreceptor Cells Calmodulin-Binding Proteins Cytoskeletal Proteins Humans Genetic Therapy Machine Learning Cell Cycle Proteins
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A review of traditional and machine learning methods applied to animal breeding.

Animal health research reviews Jun 1, 2019
The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourl...
Animal Husbandry Machine Learning Breeding Animals Livestock
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Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings] Jun 1, 2019
Learning to get by without an arm or hand can be very challenging, and existing prostheses do not yet fill the needs of individuals with amputations. One promising solution is to improve the feedback from the device to the user. Towards this end, we ...
Machine Learning Robotics Prosthesis Design Artificial Limbs Amputation, Surgical Feedback, Sensory Humans
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Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.

Journal of bioinformatics and computational biology Jun 1, 2019
Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the proces...
Algorithms Reproducibility of Results Transcriptome Humans Cluster Analysis Neural Networks, Computer Protein Interaction Maps Random Allocation Machine Learning Lung Neoplasms Support Vector Machine
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Neuron segmentation with deep learning.

Nature methods Jun 1, 2019
Neural Networks, Computer Neurons Calcium Machine Learning Deep Learning
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Deep learning adds an extra dimension to peptide fragmentation.

Nature methods Jun 1, 2019
Proteome Tandem Mass Spectrometry Machine Learning Peptides Deep Learning
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The Kipoi repository accelerates community exchange and reuse of predictive models for genomics.

Nature biotechnology Jun 1, 2019
Machine Learning Databases, Factual Genomics Whole Genome Sequencing Genome, Human Gene Editing Humans
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Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Clinical orthopaedics and related research Jun 1, 2019
BACKGROUND: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learni...
Female Aged Male Humans Machine Learning Retrospective Studies Registries Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Arthroplasty, Replacement, Hip Middle Aged Predictive Value of Tests Disability Evaluation Minimal Clinically Important Difference
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Editor's Spotlight/Take 5: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Clinical orthopaedics and related research Jun 1, 2019
Arthroplasty, Replacement, Hip Minimal Clinically Important Difference Male Middle Aged Predictive Value of Tests Retrospective Studies Female Arthroplasty, Replacement, Knee Aged Humans Disability Evaluation Patient Reported Outcome Measures Registries Machine Learning
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