Kooperativer Bibliotheksverbund

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Format: 1 online resource (572 pages)

Edition: 1st ed.

ISBN: 9789811270611

Additional Edition: Print version: Altman, Russ B Biocomputing 2023 - Proceedings Of The Pacific Symposium Singapore : World Scientific Publishing Company,c2022 ISBN 9789811270604

Language: English

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Format: 1 online resource (517 pages)

ISBN: 9789814644730

Note: Intro -- 2PSB20AnniversaryFinalv2 -- all-cp -- 0intro-cancerpanomics -- ching -- deshwar -- jang -- lehman -- nasser -- wu -- all-cp -- 0intro-cancerpathways -- engin -- kim -- 1. Introduction -- 2. Methods -- 2.1. Data -- 2.2. BioBin -- 2.3. ATHENA -- 2.4. Grammatical Evolution Neural Networks (GENN) -- 2.5. Survival fitness function -- 2.6. Experiment setup -- 3. Results and Discussion -- 3.1. Binning somatic mutations using BioBin -- 3.2. GENN modeling for somatic mutation burden -- 3.3. Biological interpretation -- 4. Conclusions -- Acknowledgments -- lockwood -- poon -- tan -- yang -- all-interactions -- 1intro-interactions -- crawford -- darabos -- frost -- holzinger -- Holzinger_PSB2015_r2VIM_1Oct2014 -- 1.   Introduction -- 1.1.   Variable selection that allows for interactions -- 2.   Methods -- 2.1.   r2VIM -- 2.2.   Data Simulation -- 3.   Results -- 3.1.   Simulated Data -- 4.   Discussion -- hu -- jeff -- patel -- restrepo -- wang -- all-crowd -- 1intro-crowdsourcing -- binder -- good -- irshad -- odgers -- waldispuhl -- all-pm -- 0intro-pm -- birol -- chang -- diggans -- fan-minogue -- glicksberg -- hinterberg -- huang -- makashir -- 2. Methods -- 2.1. Statistical framework for meta-analysis of differential co-expression -- 2.1.1. s score definition -- 2.1.2. Probability distribution of s scores -- 2.2. SLE dataset selection for meta-analysis -- 2.3. Data pre-processing -- 2.4. Meta-analysis of differential gene co-expression in SLE -- 2.5. Comparison of Type I error rates and statistical power -- 2.6. Differential expression analysis -- 2.7. Identification and annotation of gene modules -- 3. Results -- 3.1. A network of genes specifically co-expressed in SLE -- 3.2. Gene co-expression modules specific to SLE -- 3.2.1. Type I interferon response. , 3.2.2. Cell movement and response to wounding -- 3.2.3. Immune defense against extracellular organisms -- 4. Discussion -- 5. Conclusions -- nie -- sengupta -- all-wkshop -- 1wkshop-human -- 2wkshop-discovery -- 3wkshp-public -- 4wkshop-bioinfo -- 5psb15-erratum.

Additional Edition: Print version: Altman, Russ B Pacific Symposium On Biocomputing 2015 Singapore : World Scientific Publishing Company,c2014 ISBN 9789814644723

Language: English

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UID:

Format: 1 online resource (762 pages)

ISBN: 9789811215636

Note: 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.

Additional Edition: Print version: Altman, Russ B Biocomputing 2020 - Proceedings Of The Pacific Symposium Singapore : World Scientific Publishing Company,c2019 ISBN 9789811215629

Language: English

Keywords: Electronic books. ; Electronic books.

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URL: https://www.worldscientific.com/worldscibooks/10.1142/11698#t=toc

UID:

Format: 1 online resource (436 pages)

ISBN: 9789814583220

Note: Intro -- 0psb-preface-14-rba.pdf -- 1cp-all -- 0cp-intro-rev.pdf -- aguiar-rev -- badea -- 1.   Introduction and motivation -- 2.   Data and preprocessing -- 2.1.   The TCGA AML dataset -- 2.2.   Generalized mutations -- 2.3.   Protein-to-protein interaction data -- 3.   Proteins mutated in AML form pp interaction cliques -- 4.   Joint analysis of gene expression data and mutations using pp interaction data -- 4.1.   The joint clustering of expression and mutation interactor data -- 4.2.   The dimensionality of the factorization -- 4.3.   Significant sample-specific mutations -- 5.   Conclusions -- 6.   Acknowledgments -- chaibubneto (1) -- gitter-rev -- hu -- mayba -- min-rev -- morgan -- 2cad-all -- 0intro-cdr-rev -- blucher -- brubaker -- ng -- yang -- yera-rev -- zhu -- 3pleiotropy-all -- 0Pleiotropy_Proceedings_Introduction.pdf -- darabos-rev -- hall -- philip -- 4pm-all -- 0pm-intro-rev -- daneshjou -- martin -- parikh -- patwardhan-rev -- 1. Introduction -- 2. Methods -- 2.1. Subject Samples -- 2.2. Genomic Library Construction, Exome Sequencing, Alignment and Variant Calling -- 2.3. Problematic Regions of the Genome -- 2.4. Case Control Analysis -- 3. Results -- 4. Discussion -- 5text-all -- 0intro-text -- clark -- funk -- han -- komandurelayavilli -- liu (1) -- malinowski (2) -- vembu-rev -- zitnik (1) -- 1NoncodingRNA_workshop_PSB2014.pdf -- 2training -- Untitled.

Additional Edition: Print version: Altman, Russ B Pacific Symposium On Biocomputing 2014 Singapore : World Scientific Publishing Company,c2013 ISBN 9789814596343

Language: English

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Format: 1 online resource (471 p.)

ISBN: 9789813279827 , 9813279826

Note: Description based upon print version of record.

Additional Edition: Print version: Altman, Russ B Biocomputing 2019 - Proceedings Of The Pacific Symposium Singapore : World Scientific Publishing Company,c2018 9789813279810

Language: English

Keywords: Electronic books.

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UID:

Format: 1 online resource (431 p.)

ISBN: 9789811250477 , 9811250472

Note: Description based upon print version of record. , 1. Introduction. , Intro -- Content -- Preface -- AI-DRIVEN ADVANCES IN MODELING OF PROTEIN STRUCTURE -- Session Introduction: AI-Driven Advances in Modeling of Protein Structure -- 1. A short retrospect -- 2. A brief outline of current research -- 3. Future developments (complexes, ligand interactions, other molecules, dynamics, language models, geometry models, sequence design) -- 4. What is needed for further progress? -- 5. Overview of papers in this session -- 5.1. Evaluating significance of training data selection in machine learning -- 5.2. Geometric pattern transferability , 5.3. Supervised versus unsupervised sequence to contact learning -- 5.4. Side chain packing using SE(3) transformers -- 5.5. Feature selection in electrostatic representations of ligand binding sites -- References -- Training Data Composition Affects Performance of Protein Structure Analysis Algorithms -- 1. Introduction -- 2. Methods -- 2.1. Experimental Design -- 2.2. Task-specific Methods -- 3. Results -- 3.1. Performance on NMR and cryo-EM structures is consistently lower than performance on X-ray structures, independent of training set , 3.2. Inclusion of NMR data in the training set improves performance on held-out NMR data and does not degrade performance on X-ray data -- 3.3. Known biochemical and biophysical effects are replicated in trained models -- 3.4. Downsampling X-ray structures during training negatively affects performance on all types of data -- 4. Conclusion -- 5. Acknowledgments -- References -- Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Datasets -- 3.2. Contact extraction , 3.3. Representing contact geometry -- 4. Results -- 4.1. Protein self-contacts exhibit clear geometric clustering -- 4.2. Many geometric patterns transfer to protein-ligand contacts -- 4.3. Application to protein-ligand docking -- 5. Conclusion and Future Work -- Supplemental Material, Code, and Data Availability -- Acknowledgments -- References -- Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention -- 1. Introduction -- 2. Background -- 3. Methods -- 3.1. Potts Models -- 3.2. Factored Attention -- 3.3. Single-layer attention , 3.4. Pretraining on Sequence Databases -- 3.5. Extracting Contacts -- 4. Results -- 5. Discussion -- Acknowledgements -- References -- Side-Chain Packing Using SE(3)-Transformer -- 1. Introduction -- 2. Methods -- 2.1. Neighborhood Graph Representation -- 2.2. The SE(3)-Transformer Architecture -- 2.3. Node Features -- 2.4. Final Layer -- 2.5. Rotamer Selection -- 2.6. Experiments -- 3. Results -- 4. Conclusion -- 5. Acknowledgements -- 6. References -- DeepVASP-E: A Flexible Analysis of Electrostatic Isopotentials for Finding and Explaining Mechanisms that Control Binding Specificity

Additional Edition: Print version: Altman, Russ B Biocomputing 2022 - Proceedings Of The Pacific Symposium Singapore : World Scientific Publishing Company,c2021 9789811250460

Language: English

Keywords: Electronic books.

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