AIMC Topic: Algorithms

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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...
Quantitative Structure-Activity Relationship Imidazoles Algorithms p38 Mitogen-Activated Protein Kinases Protein Kinase Inhibitors Datasets as Topic Fuzzy Logic
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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...
Protein Interaction Maps Reproducibility of Results Humans Lung Neoplasms Transcriptome Cluster Analysis Support Vector Machine Random Allocation Neural Networks, Computer Machine Learning Algorithms
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Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification.

Cognitive science Jun 1, 2019
Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face - judgments that can have important societal and personal consequences. In recent years, deep...
Sociological Factors Neural Networks, Computer Algorithms Facial Recognition Deep Learning Humans
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Response to Comment on "MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery".

Annals of surgery Jun 1, 2019
Machine Learning Algorithms
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Machine learning, practically speaking.

Nature methods Jun 1, 2019
Artificial Intelligence Biomedical Research Humans Machine Learning Algorithms
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Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.

Annals of oncology : official journal of the European Society for Medical Oncology Jun 1, 2019
INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algori...
Predictive Value of Tests Programmed Cell Death 1 Receptor Humans Survival Rate Artificial Intelligence Lung Neoplasms Immunotherapy Antineoplastic Combined Chemotherapy Protocols Carcinoma, Non-Small-Cell Lung Follow-Up Studies Machine Learning Algorithms Prognosis Tomography, X-Ray Computed Melanoma
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Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.

Abdominal radiology (New York) Jun 1, 2019
PURPOSE: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cel...
Deep Learning Contrast Media Diagnosis, Differential Male Middle Aged Multidetector Computed Tomography Carcinoma, Renal Cell Adenoma, Oxyphilic Radiographic Image Interpretation, Computer-Assisted Kidney Neoplasms Iohexol Retrospective Studies Female Aged Aged, 80 and over Adult Sensitivity and Specificity Humans Software Algorithms
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A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.

Drug safety Jun 1, 2019
INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10t...
ROC Curve Drug-Related Side Effects and Adverse Reactions Algorithms Humans International Classification of Diseases Hospitalization Clinical Coding Machine Learning Hospitals Adverse Drug Reaction Reporting Systems
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DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.

Bioinformatics (Oxford, England) Jun 1, 2019
MOTIVATION: Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into host cells and cause diseases. Since T3SS secreted effectors (T3SEs) play important roles in pathogen-...
Gram-Negative Bacteria Genome, Bacterial Software Algorithms Bacterial Proteins Neural Networks, Computer
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Bastion3: a two-layer ensemble predictor of type III secreted effectors.

Bioinformatics (Oxford, England) Jun 1, 2019
MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-hos...
Bacterial Proteins Machine Learning Amino Acid Sequence Software Gram-Negative Bacteria Algorithms Computational Biology
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