AI Medical Compendium Journal:
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Showing 51 to 60 of 76 articles

Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Accurately estimating performance accuracy of machine learning classifiers is of fundamental importance in biomedical research with potentially societal consequences upon the deployment of bestperforming tools in everyday life. Although classificatio...
Computational Biology Models, Statistical Classification Machine Learning Algorithms Computer Simulation Humans
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Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answ...
Markov Chains Unified Medical Language System PubMed Knowledge Bases Data Mining Computational Biology Databases, Genetic Reproducibility of Results Humans Deep Learning Algorithms Genetic Association Studies Decision Support Techniques
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Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Phylogeography research involving virus spread and tree reconstruction relies on accurate geographic locations of infected hosts. Insufficient level of geographic information in nucleotide sequence repositories such as GenBank motivates the use of na...
Neural Networks, Computer Computational Biology Natural Language Processing Metadata Databases, Nucleic Acid Phylogeography
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DNA Steganalysis Using Deep Recurrent Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in convent...
Sequence Analysis, DNA DNA Information Theory Communications Media Neural Networks, Computer Deep Learning Computational Biology Humans
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Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and...
Models, Genetic Sequence Analysis, DNA Molecular Sequence Annotation Databases, Nucleic Acid Genome-Wide Association Study Polymorphism, Single Nucleotide Computational Biology Algorithms Deep Learning Humans Neural Networks, Computer
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DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or ...
Protein Domains Amino Acid Sequence Deep Learning Computational Biology Databases, Protein Neural Networks, Computer Algorithms
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Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting...
Humans Deep Learning Computational Biology Medical Informatics Neural Networks, Computer Machine Learning Diagnosis, Computer-Assisted Medical Informatics Computing Medical Informatics Applications
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The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervis...
Algorithms Phenotype Databases, Factual Electronic Health Records Deep Learning Computational Biology Logistic Models Humans Medical Informatics Neural Networks, Computer Machine Learning
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Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding mo...
Machine Learning Phenotype Databases, Factual Humans Medical Informatics Semantics Deep Learning International Classification of Diseases Computational Biology Natural Language Processing
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Machine learning and deep analytics for biocomputing: call for better explainability.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...
Decision Support Techniques Gene Expression Profiling Single-Cell Analysis Computational Biology Cluster Analysis Artificial Intelligence Humans Machine Learning Neural Networks, Computer Algorithms
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