AIMC Topic: Computational Biology

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A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

Nature communications Feb 5, 2020
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Can...
Humans Female Reproducibility of Results Male Mutation Whole Genome Sequencing Neoplasm Metastasis Genome, Human Neoplasms Deep Learning Computational Biology
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Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.

Journal of chemical information and modeling Feb 3, 2020
Machine learning approaches have had tremendous success in various disciplines. However, such success highly depends on the size and quality of datasets. Scientific datasets are often small and difficult to collect. Currently, improving machine learn...
Deep Learning Machine Learning Computational Biology Solubility
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miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator.

Scientific reports Jan 30, 2020
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Man...
MicroRNAs Models, Statistical Gene Expression Regulation Models, Theoretical Support Vector Machine Humans Computational Biology
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Combining deep learning with token selection for patient phenotyping from electronic health records.

Scientific reports Jan 29, 2020
Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information a...
Humans Neural Networks, Computer Artificial Intelligence Machine Learning Computational Biology Chronic Pain Biostatistics Phenotype Electronic Health Records Deep Learning
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DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Computational and mathematical methods in medicine Jan 28, 2020
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and e...
Breast Neoplasms Computational Biology Humans Algorithms Breast Mammography Female Neural Networks, Computer Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity
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Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing.

Computational biology and chemistry Jan 24, 2020
Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the deve...
Machine Learning Anti-Bacterial Agents Microbial Sensitivity Tests Computational Biology Quinolines Molecular Docking Simulation DNA Gyrase Escherichia coli Drug Design Topoisomerase II Inhibitors Molecular Structure Quantitative Structure-Activity Relationship
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Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

Computational and mathematical methods in medicine Jan 24, 2020
To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variabi...
Computational Biology Humans Radiographic Image Interpretation, Computer-Assisted Lymph Nodes Tomography, X-Ray Computed Neural Networks, Computer Information Storage and Retrieval Unsupervised Machine Learning Algorithms Diagnosis, Computer-Assisted Deep Learning
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Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation.

Computational and mathematical methods in medicine Jan 24, 2020
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion re...
Tomography, Emission-Computed, Single-Photon Magnetic Resonance Imaging Multimodal Imaging Positron-Emission Tomography Neural Networks, Computer Image Interpretation, Computer-Assisted Algorithms Databases, Factual Tomography, X-Ray Computed Humans Brain Neoplasms Computational Biology
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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.

Molecular medicine reports Jan 23, 2020
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms o...
Databases, Genetic Age Factors Gene Ontology Biomarkers Gene Expression Regulation, Neoplastic Reproducibility of Results ROC Curve Shock, Septic Infant Child, Preschool Gene Regulatory Networks Support Vector Machine Gene Expression Profiling Child Computational Biology Humans Adolescent
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A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

Scientific reports Jan 22, 2020
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncover...
Humans Metabolomics Cell Line Cell Survival Mice Autophagy Huntington Disease Machine Learning Computational Biology Genomics Adenosine Triphosphate Animals Proteomics Gene Regulatory Networks
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