|
|
|
|
| LEADER |
11043nam a22004813i 4500 |
| 001 |
EBC6823353 |
| 003 |
MiAaPQ |
| 005 |
20231204023220.0 |
| 006 |
m o d | |
| 007 |
cr cnu|||||||| |
| 008 |
231204s2021 xx o ||||0 eng d |
| 020 |
|
|
|a 9789811250477
|q (electronic bk.)
|
| 020 |
|
|
|z 9789811250460
|
| 035 |
|
|
|a (MiAaPQ)EBC6823353
|
| 035 |
|
|
|a (Au-PeEL)EBL6823353
|
| 035 |
|
|
|a (OCoLC)1321798121
|
| 040 |
|
|
|a MiAaPQ
|b eng
|e rda
|e pn
|c MiAaPQ
|d MiAaPQ
|
| 100 |
1 |
|
|a Altman, Russ B.
|
| 245 |
1 |
0 |
|a Biocomputing 2022 - Proceedings Of The Pacific Symposium.
|
| 250 |
|
|
|a 1st ed.
|
| 264 |
|
1 |
|a Singapore :
|b World Scientific Publishing Company,
|c 2021.
|
| 264 |
|
4 |
|c Ã2022.
|
| 300 |
|
|
|a 1 online resource (431 pages)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 505 |
0 |
|
|a 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.
|
| 505 |
8 |
|
|a 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 -- 1. Introduction -- 2. Methods -- 2.1. Convolutional Neural Network -- 2.2. Experimental Design -- 2.3. Comparison with Existing Methods -- 3. Results -- 4. Conclusions -- Acknowledgements -- References -- BIG DATA IMAGING GENOMICS -- Session Introduction: Big Data Imaging Genomics -- 1. Introduction -- 2. Overview of Contributions -- References -- A New Mendelian Randomization Method to Estimate Causal Effects of Multivariable Brain Imaging Exposures -- 1. Introduction -- 2. Methods -- 2.1. Step 1 : Mendelian randomization analysis on a single imaging exposure -- 2.2. Step 2: Joint instrumental variables and imaging exposures selection -- 2.3. Step 3: Causal effect identification for multiple imaging exposures -- 3. Application to evaluate the causal effect of white matter microstructure integrity on cognitive function.
|
| 505 |
8 |
|
|a 3.1. Data and study cohort -- 3.2. Results -- 4. Simulation -- 5. Discussion -- Funding -- Availability of data and materials -- Authors' contributions -- References -- Efficient Differentially Private Methods for a Transmission Disequilibrium Test in Genome Wide Association Studies -- 1. Introduction -- 2. Preliminaries -- 2.1. TDT -- 2.2. Differential Privacy -- 3. Methods -- 3.1. Exact Algorithm -- 3.2. Approximation Algorithm -- 4. Experiments -- 4.1. Simulation Data -- 4.2. Results -- 4.2.1. Run Time -- 4.2.2. Accuracy -- 4.3. Real Data -- 5. Conclusion -- Acknowledgement -- References -- Identifying Imaging Genetic Associations via Regional Morphometricity Estimation -- 1. Introduction -- 2. Methods -- 3. Materials -- 4. Experimental Design -- 5. Results and Discussion -- 6. Conclusion -- Acknowledgements -- References -- Identifying Highly Heritable Brain Amyloid Phenotypes Through Mining Alzheimer's Imaging and Sequencing Biobank Data -- 1. Introduction -- 2. Method -- 3. Materials -- 4. Experimental Workow -- 5. Results and Discussion -- 6. Conclusion -- Acknowledgements -- References -- Effects of ApoE4 and ApoE2 Genotypes on Subcortical Magnetic Susceptibility and Microstructure in 27,535 Participants from the UK Biobank -- 1. Introduction -- 2. Methods -- 2.1. UK Biobank Participants -- 2.2. T1-Weighted MRI -- 2.3. Quantitative Magnetic Susceptibility -- 2.4. Diffusion-Weighted MRI -- 2.5. Statistical Analyses -- 3. Results -- 3.1. ApoE4 Microstructural Associations -- 3.2. ApoE2 Microstructural Associations -- 3.3. ApoE-by-Age Interactions -- 3.3.1. ApoE Associations Stratified by Age -- 4. Discussion -- References -- Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product -- 1. Introduction -- 2. Methods. -- 2.1 Participants.
|
| 505 |
8 |
|
|a 2.2 Major Depressive Disorder Classification -- 2.3 Imaging Protocol and Processing -- 2.4 Calculation of linear indices of similarity -- 2.5 Calculation of QRI -- 2.7 Cognitive assessment -- 2.8 Statistics -- 3. Results -- 3.1 Group differences in symptoms and biomarkers -- 3.2 Effects of MDD on cognition. -- 3.3. Cognitive association -- 4. Discussion. -- 5. Conclusion -- 6. Acknowledgement -- References -- Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types -- 1. Introduction -- 2. Related Work -- 3. Cohort -- 3.1. Cohort Selection -- 3.2. Feature Extraction -- 4. Methods -- 4.1. Model -- 4.2. Training -- 4.2.1. Pre-Training -- 4.2.2. Meta-Training -- 4.3. Meta-Validation and Meta-Test -- 4.4. Experiments -- 4.4.1. Cancer Types -- 4.4.2. Batch Effects -- 5. Results -- 5.1. Cancer Types -- 5.2. Batch Effects -- 5.2.1. Image Resolution -- 5.2.2. Image Brightness -- 6. Discussion -- Software and Data -- References -- HUMAN INTRIGUE: META-ANALYSIS APPROACHES FOR BIG QUESTIONS WITH BIG DATA WHILE SHAKING UP THE PEER REVIEW PROCESS -- Session Introduction: Human Intrigue: Meta-Analysis Approaches for Big Questions with Big Data While Shaking Up the Peer Review Process -- 1. Introduction -- 2. The Crowd Peer Review Process -- 2.1 Reviewer's Feedback -- 2.2 Conclusions -- 3. Meta-Analysis in Biocomputing -- 3.1 Novel Methods for Meta-Analysis of 'Omics Data -- 3.2 Using Publicly Available Data in Methods Development -- 3.3 Studying the Structure of Publicly Available Data -- 3.4 Conclusions -- Acknowledgements -- References -- Multitask Group Lasso for Genome Wide Association Studies in Diverse Populations -- 1. Introduction -- 2. Methods -- 2.1. Population stratification -- 2.2.1. Adjacency-constrained hierarchical clustering -- 2.2.2. LD-groups across populations -- 2.3. Multitask group Lasso.
|
| 505 |
8 |
|
|a 2.3.1. General framework and problem formulation -- 2.3.2. Related work -- 2.3.3. Gap safe screening rules -- 2.4. Stability selection -- 3. Experiments -- 3.1. Data -- 3.2. Preprocessing -- 3.3. Comparison partners -- 4. Results -- 4.1. MuGLasso draws on both LD-groups and the multitask approach to recover disease SNPs -- 4.2. MuGLasso provides the most stable selection -- 4.3. MuGLasso selects both task-speci c and global LD-groups -- 5. Discussion and Conclusions -- Acknowledgments -- Supplementary Materials and code -- References -- Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction Using Spatially Localized Immuno-Oncology Markers -- 1. Introduction -- 2. Motivation for Comparison Study -- 2.1. Review of Prior Spatial Omics Analysis Methods -- 2.2. Motivation for Mixed Effects Machine Learning Approaches -- 3. Materials and Methods -- 3.1. Data Acquisition and Preprocessing -- 3.2. Experimental Design: Prediction Tasks and Modeling Approaches -- 4. Results -- 4.1. Macro: Inter-Tumoral Prediction -- 4.2. METS: Nodal and Distant Metastasis Prediction -- 5. Discussion -- 6. Conclusion -- 7. Acknowledgements -- 8. References -- Improving QSAR Modeling for Predictive Toxicology Using Publicly Aggregated Semantic Graph Data and Graph Neural Networks -- 1. Introduction -- 2. Methods -- 2.1. Obtaining toxicology assay data -- 2.2. Aggregating publicly available multimodal graph data -- 2.3. Heterogeneous graph neural network -- 2.3.1. Node classification -- 2.4. Baseline QSAR classifiers -- 3. Results -- 3.1. GNN node classification performance vs. baseline QSAR models -- 3.2. Ablation analysis of graph components' inuence on the trained model -- 4. Discussion -- 4.1. GNNs versus traditional ML for QSAR modeling -- 4.2. Interpretability of GNNs in QSAR -- 4.3. Sources of bias and their effects on QSAR for toxicity prediction.
|
| 505 |
8 |
|
|a 5. Conclusions.
|
| 588 |
|
|
|a Description based on publisher supplied metadata and other sources.
|
| 590 |
|
|
|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Dunker, A Keith.
|
| 700 |
1 |
|
|a Hunter, Lawrence.
|
| 700 |
1 |
|
|a Ritchie, Marylyn D.
|
| 700 |
1 |
|
|a Murray, Tiffany A.
|
| 700 |
1 |
|
|a Klein, Teri E.
|
| 776 |
0 |
8 |
|i Print version:
|a Altman, Russ B
|t Biocomputing 2022 - Proceedings Of The Pacific Symposium
|d Singapore : World Scientific Publishing Company,c2021
|z 9789811250460
|
| 797 |
2 |
|
|a ProQuest (Firm)
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6823353
|z Click to View
|
