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Benchmarking machine learning models on multi-centre eICU critical care dataset.
PloS one
Jul 2, 2020
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and publi...
Predicting Binding from Screening Assays with Transformer Network Embeddings.
Journal of chemical information and modeling
Jul 1, 2020
Cheminformatics aims to assist in chemistry applications that depend on molecular interactions, structural characteristics, and functional properties. The arrival of deep learning and the abundance of easily accessible chemical data from repositories...
Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.
JAMA network open
Jul 1, 2020
IMPORTANCE: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.
Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.
ACS synthetic biology
Jun 30, 2020
The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We deve...
Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.
Molecular pharmaceutics
Jun 19, 2020
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...
SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing.
International journal of computer assisted radiology and surgery
Jun 18, 2020
PURPOSE: In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need larg...
KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.
Genome biology
Jun 17, 2020
Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing...
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.
Journal of computer-aided molecular design
Jun 16, 2020
Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological...
Artificial intelligence: improving the efficiency of cardiovascular imaging.
Expert review of medical devices
Jun 16, 2020
INTRODUCTION: Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or p...
Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.
eNeuro
Jun 15, 2020
Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean=0.95, SD<0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipu...
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