AIMC Topic: Datasets as Topic

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Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.

Journal of digital imaging Aug 1, 2019
Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded...
Image Processing, Computer-Assisted Radiography Datasets as Topic Functional Laterality Reproducibility of Results Sensitivity and Specificity Deep Learning Humans Algorithms Retrospective Studies
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Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation.

Journal of digital imaging Aug 1, 2019
To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect...
Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Ankle Humans Ankle Fractures Neural Networks, Computer Sensitivity and Specificity Radiography
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mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research Jul 2, 2019
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...
Datasets as Topic Crystallography, X-Ray Binding Sites Thermodynamics Proteins Internet Benchmarking Protein Conformation, beta-Strand Mutation, Missense Protein Interaction Mapping Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Humans Machine Learning Software Protein Binding
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PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research Jul 2, 2019
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...
Humans Machine Learning Software Binding Sites Thermodynamics Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Benchmarking Amino Acid Sequence Ligands Protein Binding Datasets as Topic Proteins Internet
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Inhibition activity prediction for a dataset of candidates' drug by combining fuzzy logic with MLR/ANN QSAR models.

Chemical biology & drug design Jun 1, 2019
A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effecti...
Algorithms Datasets as Topic Fuzzy Logic Quantitative Structure-Activity Relationship Protein Kinase Inhibitors Imidazoles p38 Mitogen-Activated Protein Kinases
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Comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2019
Neural Networks, Computer Deep Learning Social Media Natural Language Processing Humans Drug-Related Side Effects and Adverse Reactions Data Mining Pharmacovigilance Datasets as Topic
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Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs.

Journal of digital imaging Jun 1, 2019
Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs of OA, 2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading ...
Predictive Value of Tests Datasets as Topic Automation Female Osteoarthritis, Knee Humans Male Neural Networks, Computer Middle Aged Algorithms Severity of Illness Index
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SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Nucleic acids research May 7, 2019
Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task....
Mice Cell Lineage Humans Sequence Analysis, RNA Pancreatic Neoplasms Gene Expression Profiling Software Gene Expression Regulation, Neoplastic RNA, Small Cytoplasmic Supervised Machine Learning Transcriptome Animals Cluster Analysis High-Throughput Nucleotide Sequencing Single-Cell Analysis Datasets as Topic
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DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Nucleic acids research May 7, 2019
Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predic...
Neoplasm Proteins Mutation Datasets as Topic Monte Carlo Method Cell Cycle Proteins Atlases as Topic Oncogenes Gene Expression Regulation, Neoplastic Cell Proliferation Nuclear Proteins Humans Cell Movement Software Machine Learning Neoplasms Cell Line, Tumor
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deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.

Journal of the American Medical Informatics Association : JAMIA May 1, 2019
OBJECTIVE: In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties...
Biological Ontologies Vocabulary, Controlled Natural Language Processing Neural Networks, Computer Medical Subject Headings Data Mining Algorithms Datasets as Topic Unified Medical Language System Deep Learning Systematized Nomenclature of Medicine
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