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  • 1
    UID:
    almahu_9949301201802882
    Umfang: 1 online resource (762 pages)
    ISBN: 9789811215636
    Anmerkung: Intro -- Content -- Preface -- ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE -- Session Introduction: Artificial Intelligence for Enhancing Clinical Medicine -- 1. Introduction -- 2. Novel Research Applying Artificial Intelligence to Clinical Medicine -- 2.1. Artificial intelligence for predicting patient outcomes -- 2.2. Artificial intelligence for improved insight into disease pathogenesis and features -- 3. Artificial intelligence for advancing medical workflows -- 4. Artificial intelligence for improving imaging -- 5. Conclusion -- References -- Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model -- 1. Introduction -- 2. Methods -- 2.1. The Longitudinal Joint Learning Model -- 2.2. The Solution Algorithm Using the Multi-Block ADMM -- 3. Experiments -- 3.1. Performance -- 3.2. Empirical Convergence -- 3.3. Biomarker Identification -- 4. Conclusion -- Acknowledgements -- References -- Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Data collection and preparation -- 3.2. Evaluation with GHKG -- 3.3. Disease predictability analysis -- 3.4. Demographic analysis -- 3.5. Non-linear methods -- 4. Results -- 5. Discussion -- 5.1. Data size does not always matter. -- 5.2. Confounders may explain errors -- 5.3. Increased model complexity does not necessarily help -- 5.4. Limitations remain as an opportunity for future work -- 6. Conclusion -- Acknowledgements -- References -- Increasing Clinical Trial Accrual via Automated Matching of Biomarker Criteria -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1. Specimens and Retrospective Analysis -- 2.2. Real-time Analysis -- 2.3. Source of Biomarker-based Clinical Trial Data -- 3. RESULTS. , 3.1. STAMP assay identifies somatic mutations -- 3.2. Algorithmic pipeline flags eligible patients for precision medicine clinical trials -- 3.2.1. Automation of Feature Matching -- 3.2.2. Manual Review of Matching Output -- 3.3. Validation of algorithmic pipeline -- 3.4. Match rate analysis of STAMP-identified mutations -- 4. DISCUSSION -- 4.1. Incorporation of informatics into clinical workflows -- 4.2. Limitations of algorithmic pipelines -- 5. CONCLUSION -- 6. AUTHOR CONTRIBUTIONS -- 7. ACKNOWLEDGEMENTS -- 8. REFERENCES -- 9. FIGURES -- 10. SUPPLEMENTARY TABLES AND FIGURES -- Addressing the Credit Assignment Problem in Treatment Outcome Prediction Using Temporal Difference Learning -- 1. Introduction -- 2. Dataset -- 3. Methods -- 3.1. Feature Extraction -- 3.2. Temporal Difference Learning -- 3.2.1. State-Estimation -- 3.2.2. Value Iteration -- 3.2.3. Optimization -- 3.3. Baselines and Performance Measure -- 4. Results -- 5. Discussion and Conclusion -- References -- Multiclass Disease Classification from Microbial Whole-Community Metagenomes -- 1. Introduction -- 2. Previous Work -- 3. Problem Setup -- 3.1. Dataset Construction -- 3.2. Graph Convolutional Neural Networks -- 3.3. Models -- 3.4. Training -- 4. Results -- 5. Conclusion -- 6. Acknowledgments -- 7. External Links -- References -- LitGen: Genetic Literature Recommendation Guided by Human Explanations -- 1. Introduction -- 2. Clinical Variant Curation Data -- 2.1. ClinGen's Variant Curation Interface (VCI) -- 2.2. Labeled papers -- 2.3. Unlabeled papers -- 3. Method -- 3.1. BiLSTM baseline -- 3.2. Leveraging unlabeled data -- 3.3. Explanations in multitask learning -- 3.4. Explanations as feature selection for proxy labeling -- 4. Experimental results -- 4.1. Evaluation metrics -- 4.2. Performance comparison -- 4.3. Performance of Proxy Labeling Model. , 4.4. Performance by Evidence Types -- 5. Discussion -- References -- From Genome to Phenome: Predicting Multiple Cancer Phenotypes Based on Somatic Genomic Alterations via the Genomic Impact Transformer -- 1. Introduction -- 2. Materials and methods -- 2.1. SGAs and DEGs pre-processing -- 2.2. The GIT neural network -- 2.2.1. GIT network structure: encoder-decoder architecture -- 2.2.2. Pre-training gene embeddings using Gene2Vec algorithm -- 2.2.3. Encoder: multi-head self-attention mechanism -- 2.2.4. Decoder: multi-layer perceptron (MLP) -- 2.3. Training and evaluation -- 3. Results -- 3.1. GIT statistically detects real biological signals -- 3.2. Gene embeddings compactly represent the functional impact of SGAs -- 3.4. Personalized tumor embeddings reveal distinct survival profiles -- 3.5. Tumor embeddings are predictive of drug responses of cancer cell lines -- 4. Conclusion and Future Work -- Acknowledgments -- Funding -- References -- Automated Phenotyping of Patients with Non-Alcoholic Fatty Liver Disease Reveals Clinically Relevant Disease Subtypes -- 1. Introduction -- 2. Methods -- 2.1. NAFLD definition -- 2.2. Natural language processing -- 2.3. Data collection -- 2.4. Clinical feature standardization and quality control -- 2.4.1. Demographic data -- 2.4.2. Diagnoses, procedures, medications -- 2.4.3. Laboratory tests -- 2.4.4. Vital signs -- 2.5. Patient pairwise distance and clustering -- 2.6. Statistical analysis -- 2.6.1. Descriptive statistics -- 2.6.2. Survival analysis -- 3. Results -- 3.1. Descriptive statistics for the cohort -- 3.2. Identification of NAFLD subtypes -- 3.3. Identification of distinct outcomes by NAFLD subtype -- 3.4. Internal cross-validation of the subtypes discovered -- 4. Conclusion -- 5. References -- References -- Monitoring ICU Mortality Risk with a Long Short-Term Memory Recurrent Neural Network. , 1. Introduction -- 2. Background and Related Work -- 3. Data and Preprocessing -- 3.1. Data Source and Cohort Selection -- 3.2. Data Extraction and Preprocessing -- 4. Methodology -- 4.1. Mortality Monitoring Task -- 4.2. Average Pooling and Attention Mechanism -- 4.3. Recurrent Neural Network (RNN) -- 5. Experimental Design -- 5.1. Sampling Strategy -- 5.2. Baseline Model -- 5.3. Experimental Settings -- 6. Results and Analysis -- 6.1. Dimensionality Analysis -- 6.2. Prediction with Different Feature Representations -- 6.3. Interpreting Mortality of Learned Representation -- 7. Discussion and Conclusions -- References -- Multilevel Self-Attention Model and Its Use on Medical Risk Prediction -- 1. Introduction -- 2. Related Work -- 2.1. Future disease prediction -- 3. Methods -- 3.1. Terminology and Notation -- 3.2. Model Architecture -- 3.3. Self-attention Encoder Unit -- 3.4. Loss Function -- 4. Experiments -- 4.1. Source of Data -- 4.2. Dataset preprocessing -- 4.3. Implementation details -- 5. Results -- 5.1. Future disease prediction -- 5.2. Future cost prediction -- 5.3. Case study for the self-attention mechanism -- 6. Conclusion -- 7. Bibliography -- Identifying Transitional High Cost Users from Unstructured Patient Profiles Written by Primary Care Physicians -- 1. Introduction -- 2. Data -- 2.1. EMRPC -- 2.2. Total Healthcare Costs -- 2.3. Encoding of Ordinal Variables -- 2.4. Word Embeddings -- 3. Methods -- 3.1. Bag of Words -- 3.2. EmbEncode -- 3.3. Historical Baseline -- 3.4. Varying the Training Set -- 3.5. Varying the Evaluation Set -- 4. Results -- 5. Discussion -- Acknowledgments -- References -- Obtaining Dual-Energy Computed Tomography (CT) Information from a Single-Energy CT Image for Quantitative Imaging Analysis of Living Subjects by Using Deep Learning -- 1. Introduction -- 2. Methods -- 3. Results. , 4. Discussion and Conclusion -- 5. Acknowledge -- References -- INTRINSICALLY DISORDERED PROTEINS (IDPS) AND THEIR FUNCTIONS -- Session Introduction: On the Importance of Computational Biology and Bioinformatics to the Origins and Rapid Progression of the Intrinsically Disordered Proteins Field -- 1. Introduction -- 2. Computational prediction of IDPs and IDRs and their functions -- 3. Popularization of research on IDPs and IDRs -- 4. A Collection of Recent Papers on IDPs and IDRs -- References -- Many-to-One Binding by Intrinsically Disordered Protein Regions -- 1. Introduction -- 2. Results -- 2.1. Many-to-one binding datasets -- 2.2. Many-to-one binding profiles: independent and overlapping -- 2.3 Comparing VOR (with backbone only) and RMSΔASA Values -- 2.4. Selected many-to-one case studies -- 3. Discussion -- 4. Materials and Methods -- 4.1. Dataset preparation -- 4.2. MoRF sequence similarity -- 4.3. MoRF interface similarity -- References -- Disordered Function Conjunction: On the In-Silico Function Annotation of Intrinsically Disordered Regions -- 1. Introduction -- 2. Materials and Methods -- 2.1. Data collection -- 2.2. Computational workflow -- 2.2.1. Feature-based representation of protein regions -- 2.2.2. Prediction of protein region functions -- 2.2.3. Assessment of the function prediction and clustering -- 3. Results and Discussion -- 3.1. Prediction of individual functions of IDRs -- 3.2. IDRs described in multidimensional space form function-related clusters -- 3.3. Case studies -- 4. Conclusions -- Acknowledgments -- References -- De novo Ensemble Modeling Suggests that AP2-Binding to Disordered Regions Can Increase Steric Volume of Epsin but Not Eps15 -- 1. Introduction -- 2. Methods -- 2.1. Generation of structural ensembles -- 2.2. Filtering Epsin conformers to mimic the effect of Plasma membrane. , 2.3. Docking AP2α to the IDRs by superposition.
    Weitere Ausg.: Print version: Altman, Russ B Biocomputing 2020 - Proceedings Of The Pacific Symposium Singapore : World Scientific Publishing Company,c2019 ISBN 9789811215629
    Sprache: Englisch
    Schlagwort(e): Electronic books. ; Electronic books.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    UID:
    gbv_1785440195
    Umfang: 1 Online-Ressource (xiv, 747 Seiten)
    ISBN: 9789811215636
    Inhalt: The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3 -7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
    Anmerkung: Description based on publisher supplied metadata and other sources
    Weitere Ausg.: ISBN 9789811215629
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 9789811215629
    Sprache: Englisch
    Schlagwort(e): Biocomputer ; Bioinformatik ; Kongress ; Konferenzschrift
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    UID:
    edoccha_9959213352502883
    Umfang: 1 online resource (500 p.) : , ill.
    ISBN: 981-12-1563-4
    Inhalt: "The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3-7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field."--Publisher's website.
    Anmerkung: "December 2019." , Session introduction: Artificial intelligence for enhancing clinical medicine / Roxana Daneshjou ... [et al.] -- Predicting longitudinal outcomes of Alzheimer's disease via a tensor-based joint classification and regression model / Lodewijk Brand ... [et al.] -- Robustly extracting medical knowledge from EHRs: a case study of learning a health knowledge graph / Irene Y. Chen ... [et al.] -- Increasing clinical trial accrual via automated matching of biomarker criteria / Jessica W. Chen ... [et al.] -- Addressing the credit assignment problem in treatment outcome prediction using temporal difference learning / Sahar Harati ... [et al.] -- Multiclass disease classification from microbial whole-community metagenomes / Saad Khan and Libusha Kelly -- LitGen: genetic literature recommendation guided by human explanations / Allen Nie ... [et al.] -- From genome to phenome: predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer / Yifeng Tao ... [et al.] -- Automated phenotyping of patients wsith non-alcolholic fatty liver disease reveals clinically relevant disease subtypes / Maxence Vandromme [et al.] -- Monitoring ICU mortality risk with a long short-term memory recurrent neural network / Ke Yu ... [et al.] -- Multilevel self-attention model and its use on medical risk prediction / Xianlong Zeng ... [et al.] -- Identifying transitional high cost users from unstructured patient profiles written by primary care physicians / Haoran Zhang ... [et al.] --Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning / Wei Zhao ... [et al.] -- On the importance of computational biology and bioinformatics to the origins and rapid progression of the intrinsically disordered proteins field / Lukasz Kurgan ... [et al.] -- Many-to-one binding by intrinsically disordered problem regions / Wei-Lun Alterovitz ... [et al.] -- Disordered function conjunction: on the in-silico function annotation of intrinsically disordered regions / Sina Ghadermarzi ... [et al.] -- De novo ensemble modeling suggests that AP2-binding to disordered regions can increase steric volume of Epsin but not Eps15 / N. Suhas Jagannathan ... [et al.] -- Modulation of p53 transactivation domain conformations by ligand binding and cancer-associated mutations / Xiaorong Liu and Jianhan Chen -- Exploring relationship between the density of charged tracts within disordered regions and phase separation / Ramiz Somjee, Diana M. Mitrea and Richard W. Kriwacki -- Session introduction: Mutational signatures: etiology, properties, and role in cancer / Mark D.M. Leiserson, Teresa M. Przytycka and Roded Sharan -- PhySigs: phylogenetic inference of mutational signature dynamics / Sarah Chistensen, Mark D.M. Leiserson and Mohammed El-Kebir -- TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies / Caitlin F. Harrigan ... [et al.] -- Impact of mutational signatures on microRNA and their response elements / Eirini Stamoulakatou ... [et al.] -- DNA repair footprint uncovers contribution of DNA repair mechanism to mutational signatures / Damian Wojtowicz ... [et al.] -- Genome gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation rates / Adamo Young ... [et al.] -- Ongoing challenges and innovative approaches for recognizing pattern across large-scale, integrative biomedical datasets / Shilpa Nadimpalli ... [et al.] -- Clinical concept embeddings learned from massive sources of multimodal medical data / Andrew L. Beam ... [et al.] -- Assessment of imputation methods for missing gene expression data in meta-analysis of distinct cohorts of tuberculosis patients / Carly A. Bobak ... [et al.] -- Towards identifying drug side effects from social media using active learning and crowd sourcing / Sophie Burkhardt ... [et al.] -- Microvascular dynamics from 4D microscopy using temporal segmentation / Shir Gur ... [et al.] -- Using transcriptional signatures to find cancer drivers with LURE / David Haan ... [et al.] -- and other papers. , English
    Weitere Ausg.: ISBN 981-12-1562-6
    Sprache: Englisch
    Schlagwort(e): Conference papers and proceedings.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 4
    UID:
    almahu_9949292627402882
    Umfang: 1 online resource (500 p.) : , ill.
    ISBN: 981-12-1563-4
    Inhalt: "The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3-7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field."--Publisher's website.
    Anmerkung: "December 2019." , Session introduction: Artificial intelligence for enhancing clinical medicine / Roxana Daneshjou ... [et al.] -- Predicting longitudinal outcomes of Alzheimer's disease via a tensor-based joint classification and regression model / Lodewijk Brand ... [et al.] -- Robustly extracting medical knowledge from EHRs: a case study of learning a health knowledge graph / Irene Y. Chen ... [et al.] -- Increasing clinical trial accrual via automated matching of biomarker criteria / Jessica W. Chen ... [et al.] -- Addressing the credit assignment problem in treatment outcome prediction using temporal difference learning / Sahar Harati ... [et al.] -- Multiclass disease classification from microbial whole-community metagenomes / Saad Khan and Libusha Kelly -- LitGen: genetic literature recommendation guided by human explanations / Allen Nie ... [et al.] -- From genome to phenome: predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer / Yifeng Tao ... [et al.] -- Automated phenotyping of patients wsith non-alcolholic fatty liver disease reveals clinically relevant disease subtypes / Maxence Vandromme [et al.] -- Monitoring ICU mortality risk with a long short-term memory recurrent neural network / Ke Yu ... [et al.] -- Multilevel self-attention model and its use on medical risk prediction / Xianlong Zeng ... [et al.] -- Identifying transitional high cost users from unstructured patient profiles written by primary care physicians / Haoran Zhang ... [et al.] --Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning / Wei Zhao ... [et al.] -- On the importance of computational biology and bioinformatics to the origins and rapid progression of the intrinsically disordered proteins field / Lukasz Kurgan ... [et al.] -- Many-to-one binding by intrinsically disordered problem regions / Wei-Lun Alterovitz ... [et al.] -- Disordered function conjunction: on the in-silico function annotation of intrinsically disordered regions / Sina Ghadermarzi ... [et al.] -- De novo ensemble modeling suggests that AP2-binding to disordered regions can increase steric volume of Epsin but not Eps15 / N. Suhas Jagannathan ... [et al.] -- Modulation of p53 transactivation domain conformations by ligand binding and cancer-associated mutations / Xiaorong Liu and Jianhan Chen -- Exploring relationship between the density of charged tracts within disordered regions and phase separation / Ramiz Somjee, Diana M. Mitrea and Richard W. Kriwacki -- Session introduction: Mutational signatures: etiology, properties, and role in cancer / Mark D.M. Leiserson, Teresa M. Przytycka and Roded Sharan -- PhySigs: phylogenetic inference of mutational signature dynamics / Sarah Chistensen, Mark D.M. Leiserson and Mohammed El-Kebir -- TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies / Caitlin F. Harrigan ... [et al.] -- Impact of mutational signatures on microRNA and their response elements / Eirini Stamoulakatou ... [et al.] -- DNA repair footprint uncovers contribution of DNA repair mechanism to mutational signatures / Damian Wojtowicz ... [et al.] -- Genome gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation rates / Adamo Young ... [et al.] -- Ongoing challenges and innovative approaches for recognizing pattern across large-scale, integrative biomedical datasets / Shilpa Nadimpalli ... [et al.] -- Clinical concept embeddings learned from massive sources of multimodal medical data / Andrew L. Beam ... [et al.] -- Assessment of imputation methods for missing gene expression data in meta-analysis of distinct cohorts of tuberculosis patients / Carly A. Bobak ... [et al.] -- Towards identifying drug side effects from social media using active learning and crowd sourcing / Sophie Burkhardt ... [et al.] -- Microvascular dynamics from 4D microscopy using temporal segmentation / Shir Gur ... [et al.] -- Using transcriptional signatures to find cancer drivers with LURE / David Haan ... [et al.] -- and other papers. , English
    Weitere Ausg.: ISBN 981-12-1562-6
    Sprache: Englisch
    Schlagwort(e): Conference papers and proceedings.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 5
    UID:
    edocfu_9959213352502883
    Umfang: 1 online resource (500 p.) : , ill.
    ISBN: 981-12-1563-4
    Inhalt: "The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3-7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field."--Publisher's website.
    Anmerkung: "December 2019." , Session introduction: Artificial intelligence for enhancing clinical medicine / Roxana Daneshjou ... [et al.] -- Predicting longitudinal outcomes of Alzheimer's disease via a tensor-based joint classification and regression model / Lodewijk Brand ... [et al.] -- Robustly extracting medical knowledge from EHRs: a case study of learning a health knowledge graph / Irene Y. Chen ... [et al.] -- Increasing clinical trial accrual via automated matching of biomarker criteria / Jessica W. Chen ... [et al.] -- Addressing the credit assignment problem in treatment outcome prediction using temporal difference learning / Sahar Harati ... [et al.] -- Multiclass disease classification from microbial whole-community metagenomes / Saad Khan and Libusha Kelly -- LitGen: genetic literature recommendation guided by human explanations / Allen Nie ... [et al.] -- From genome to phenome: predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer / Yifeng Tao ... [et al.] -- Automated phenotyping of patients wsith non-alcolholic fatty liver disease reveals clinically relevant disease subtypes / Maxence Vandromme [et al.] -- Monitoring ICU mortality risk with a long short-term memory recurrent neural network / Ke Yu ... [et al.] -- Multilevel self-attention model and its use on medical risk prediction / Xianlong Zeng ... [et al.] -- Identifying transitional high cost users from unstructured patient profiles written by primary care physicians / Haoran Zhang ... [et al.] --Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning / Wei Zhao ... [et al.] -- On the importance of computational biology and bioinformatics to the origins and rapid progression of the intrinsically disordered proteins field / Lukasz Kurgan ... [et al.] -- Many-to-one binding by intrinsically disordered problem regions / Wei-Lun Alterovitz ... [et al.] -- Disordered function conjunction: on the in-silico function annotation of intrinsically disordered regions / Sina Ghadermarzi ... [et al.] -- De novo ensemble modeling suggests that AP2-binding to disordered regions can increase steric volume of Epsin but not Eps15 / N. Suhas Jagannathan ... [et al.] -- Modulation of p53 transactivation domain conformations by ligand binding and cancer-associated mutations / Xiaorong Liu and Jianhan Chen -- Exploring relationship between the density of charged tracts within disordered regions and phase separation / Ramiz Somjee, Diana M. Mitrea and Richard W. Kriwacki -- Session introduction: Mutational signatures: etiology, properties, and role in cancer / Mark D.M. Leiserson, Teresa M. Przytycka and Roded Sharan -- PhySigs: phylogenetic inference of mutational signature dynamics / Sarah Chistensen, Mark D.M. Leiserson and Mohammed El-Kebir -- TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies / Caitlin F. Harrigan ... [et al.] -- Impact of mutational signatures on microRNA and their response elements / Eirini Stamoulakatou ... [et al.] -- DNA repair footprint uncovers contribution of DNA repair mechanism to mutational signatures / Damian Wojtowicz ... [et al.] -- Genome gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation rates / Adamo Young ... [et al.] -- Ongoing challenges and innovative approaches for recognizing pattern across large-scale, integrative biomedical datasets / Shilpa Nadimpalli ... [et al.] -- Clinical concept embeddings learned from massive sources of multimodal medical data / Andrew L. Beam ... [et al.] -- Assessment of imputation methods for missing gene expression data in meta-analysis of distinct cohorts of tuberculosis patients / Carly A. Bobak ... [et al.] -- Towards identifying drug side effects from social media using active learning and crowd sourcing / Sophie Burkhardt ... [et al.] -- Microvascular dynamics from 4D microscopy using temporal segmentation / Shir Gur ... [et al.] -- Using transcriptional signatures to find cancer drivers with LURE / David Haan ... [et al.] -- and other papers. , English
    Weitere Ausg.: ISBN 981-12-1562-6
    Sprache: Englisch
    Schlagwort(e): Conference papers and proceedings.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 6
    Online-Ressource
    Online-Ressource
    New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo : World Scientific
    UID:
    b3kat_BV046668159
    Umfang: 1 Online-Ressource
    ISBN: 9789811215636
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-981-1215-62-9
    Sprache: Englisch
    Fachgebiete: Informatik
    RVK:
    Schlagwort(e): Biocomputer ; Konferenzschrift
    URL: Volltext  (kostenfrei)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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