Biocomputing 2019 - Proceedings Of The Pacific Symposium. (Kayıt no. 15554)
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| Sabit Uzunluktaki Kontrol Alanı | 11097nam a22004813i 4500 |
| 001 - KONTROL NUMARASI | |
| Control Dosyası | EBC6383183 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | MiAaPQ |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20220623112327.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 220617s2018 xx o ||||0 eng d |
| 020 ## - ISBN - ULUSLARARASI STANDART KİTAP NUMARASI | |
| Isbn | 9789813279827 |
| -- | (electronic bk.) |
| 020 ## - ISBN - ULUSLARARASI STANDART KİTAP NUMARASI | |
| Cancelled/invalid ISBN | 9789813279810 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (MiAaPQ)EBC6383183 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (Au-PeEL)EBL6383183 |
| 035 ## - SİSTEM KONTROL NUMARASI | |
| Sistem Kontrol Numarası | (OCoLC)1231609909 |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | MiAaPQ |
| Kataloglama Dili | eng |
| Açıklama Kuralları | rda |
| -- | pn |
| Çeviri Kurumu | MiAaPQ |
| Değiştiren Kurum | MiAaPQ |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Altman, Russ B. |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | Biocomputing 2019 - Proceedings Of The Pacific Symposium. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Singapore : |
| Name of producer, publisher, distributor, manufacturer | World Scientific Publishing Company, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2018. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2019. |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 1 online resource (471 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | Intro -- Preface -- PATTERN RECOGNITION IN BIOMEDICAL DATA: CHALLENGES IN PUTTING BIG DATA TO WORK -- Session introduction -- Introduction -- References -- Learning Contextual Hierarchical Structure of Medical Concepts with Poincair�e Embeddings to Clarify Phenotypes -- 1. Introduction -- 2. Methods -- 2.1. Source Code -- 2.2. Data Source -- 2.3. Data Selection and Preprocessing -- 2.3.1. Reference ICD9 Example -- 2.3.2. Real Member Analyses -- 2.4. Poincar�e Embeddings -- 2.5. Processing and Evaluating Embeddings -- 3. Results -- 3.1. ICD9 Hierarchy Evaluation -- 3.2. Poincar�e Embeddings on 10 Million Members -- 3.3. Comparison with Euclidean Embeddings -- 3.4. Cohort Specific Embeddings -- 4. Discussion and Conclusion -- 5. Acknowledgments -- References -- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data -- 1. Introduction -- 2. Background -- 2.1. Multitask nets -- 3. Methods -- 3.1. Dataset Construction and Design -- 3.2. Experimental Design -- 4. Experiments and Results -- 4.1. When Does Multitask Learning Improve Performance? -- 4.2. Relationship Between Performance and Number of Tasks -- 4.3. Comparison with Logistic Regression Baseline -- 4.4. Interaction between Phenotype Prevalence and Complexity -- 5. Limitations -- 6. Conclusion -- Acknowledgments -- References -- ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites -- 1. Introduction -- 1.1. Integrate evidence from multiple clinical sites -- 1.2. Distributed Computing -- 2. Material and Method -- 2.1. Clinical Cohort and Motivating Problem -- 2.2. Algorithm -- 2.3. Simulation Design -- 3. Results -- 3.1. Simulation Results -- 3.2. Fetal Loss Prediction via ODAL -- 4. Discussion -- References. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier -- 1. Introduction -- 2. Methods -- 2.1. Data Set and Implementation -- 2.2. Proposed PVC Detection Method -- 2.2.1. Feature Extraction -- 2.2.2. Classification -- 3. Results -- 3.1. Full Database Evaluation -- 3.2. Timing Disturbance Evaluation -- 3.3. Cross-Patient Training Evaluation -- 3.4. Estimated Parameters and Convergence -- 4. Discussion -- References -- Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications -- 1. Introduction -- 2. Related Work -- 3. Confounder Filtering (CF) Method -- 3.1. Overview -- 3.2. Method -- 3.3. Availability -- 4. Experiments -- 4.1. lung adenocarcinoma prediction -- 4.1.1. Data -- 4.1.2. Results -- 4.2. Segmentation on right ventricle(RV) of Heart -- 4.2.1. Data -- 4.2.2. Results -- 4.3. Students' confusion status prediction -- 4.3.1. Data -- 4.3.2. Results -- 4.4. Brain tumor prediction -- 4.4.1. Data -- 4.4.2. Results -- 4.5. Analyses of the method behaviors -- 5. Conclusion -- 6. Acknowledgement -- References -- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM -- 1. Introduction -- 2. METHODS -- 2.1 Data Set Preparation -- 2.2 Input Encoding -- 2.3 Model Architecture -- 2.4 Evaluation criteria -- 3. RESULTS AND DISCUSSION -- 3.1 Parameter configuration experiments on test data -- 3.2 Comparison with Other Domain Boundary Predictors -- 3.2.1 Free modeling targets from CASP 9 -- 3.2.2 Multi-domain targets from CASP 9 -- 3.2.3 Discontinuous domain target from CASP 8 -- 4. CONCLUSION -- 5. ACKNOWLEDGEMENTS -- REFERENCES -- Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions -- 1. Introduction -- 2. Background theory. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | 3. Dataset for training and testing -- 3.1. Source databases -- 3.2. Dataset generation -- 4. Methods -- 4.1. ResNet architecture in our model -- 4.2. Tandem inputs of forward- and reverse-strand sequences -- 4.3. Biallelic high-level network structure -- 4.4. Incorporating HaploReg SNP annotation features -- 4.5. Training of models -- 5. Results -- 6. Conclusions and discussion -- Acknowledgements -- References -- DNA Steganalysis Using Deep Recurrent Neural Networks -- 1. Introduction -- 2. Background -- 2.1. Notations -- 2.2. Hiding Messages -- 2.3. Determination of Message-Hiding Regions -- 3. Methods -- 3.1. Proposed DNA Steganalysis Principle -- 3.2. Proposed Steganalysis RNN Model -- 4. Results -- 4.1. Dataset -- 4.2. Input Representation -- 4.3. Model Training -- 4.4. Evaluation Procedure -- 4.5. Performance Comparison -- 5. Discussion -- Acknowledgments -- References -- Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Toponym Detection -- 3.1.1. Recurrent Neural Networks -- 3.1.2. LSTM -- 3.1.3. Other Gated RNN Architectures -- 3.1.4. Hyperparameter search and optimization -- 3.2. Toponym Disambiguation -- 3.2.1. Building Geonames Index -- 3.2.2. Searching Geonames Index -- 4. Results and Discussion -- 4.1. Toponym Disambiguation -- 4.2. Toponym Resolution -- 5. Limitations and Future Work -- 6. Conclusion -- Acknowledgments -- Funding -- References -- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning -- 1. Introduction -- 2. Related Work -- 3. Method -- 3.1. Model Framework -- 3.2. Deep Reinforcement Learning for Organizing Actions -- 3.3. Preprocessing and Name Entity Recognition with UMLS -- 3.4. Bidirectional LSTM for Relation Classification. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | 3.5. Algorithm -- 3.6. Implementation Specification -- 4. Experiments -- 4.1. Data -- 4.2. Evaluation -- 4.3. Results -- 4.3.1. Improved Reliability -- 4.3.2. Robustness in Real-world Situations -- 4.3.3. Number of Articles Read -- 5. Conclusions and Future Work -- 6. Acknowledgement -- References -- Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies -- 1. Introduction -- 2. Methods -- 2.1. Performance measures: definitions and estimation -- 2.2. Positive-unlabeled setting -- 2.3. Performance measure correction -- 3. Experiments and Results -- 3.1. A case study -- 3.2. Data sets -- 3.3. Experimental protocols -- 3.4. Results -- 4. Conclusions -- Acknowledgements -- References -- PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction -- 1. Introduction -- 2. System and methods -- 2.1. Data -- 2.2. Single views and co-training -- 2.3. Maximizing agreement across views through label assignment -- 3. Results -- 3.1. Preliminary experiments to optimize PLATYPUS performance -- 3.2. Predicting drug sensitivity in cell lines -- 3.3. Key features from PLATYPUS models -- 4. Conclusions -- Acknowledgments -- References -- Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival -- 1. Introduction -- 2. Materials and Methods -- 2.1 Data collection -- 2.2 K-mer selection -- 2.3 NGS pipeline and k-mer extraction -- 2.4 Data cleaning -- 2.5 Normalization of k-mer frequencies -- 2.6 Copy number segregation and cutoff selection -- 2.7 Validation of copy number -- 2.8 Survival analysis -- 2.9 Additional immune analysis -- 3. Results and Discussions -- 3.1 Establishing unique k-mers -- 3.2 Varying coverage of KIR region by exome capture kit -- 3.3 Inference of KIR copy number -- 3.4 Population variation of the KIR region. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | 3.5 KIR inhibitory gene burden correlates with survival in cervical and uterine cancer -- 5. Conclusions -- 6. Acknowledgements -- 7. Supplementary Material -- References -- Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier -- 1. Introduction -- 2. Data -- 2.1. Preprocessing -- 3. Deep Cancer Classifier -- 3.1. Training & -- testing -- 3.2. Parameter tuning -- 3.3. Feature importance -- 4. Results and Discussion -- 4.1. Model selection -- 4.2. Classifier performance -- 4.3. Comparison with other methods -- 4.4. Feature importance -- 5. Conclusion -- References -- Implementing and Evaluating A Gaussian Mixture Framework for Identifying Gene Function from TnSeq Data -- 1. Introduction -- 1.1. TnSeq Motivation and Background -- 1.2. Motivation and New Methods -- 2. Methods -- 2.1. TnSeq Experimental Data -- 2.2. Mixture framework -- 2.3. Classification methods -- 2.3.1. Novel method - EM -- 2.3.2. Current method - t-statistic -- 2.3.3. Bayesian hierarchical model -- 2.3.4. Data partitioning for the Bayesian model -- 2.4. Simulation -- 2.5. Real data -- 3. Results -- 3.1.1. Classification rate -- 3.1.2. False positive rate -- 3.1.3. Positive classification rate -- 3.1.4. Cross entropy -- 3.2. Simulation Results -- 3.3. Comparisons on real data -- 3.4. Software -- 4. Discussion -- References -- SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs -- 1. Introduction -- 2. Results -- 2.1. SNPs2ChIP analysis framework overview -- 2.2. Batch normalization of heterogeneous epigenetic features -- 2.3. Latent factor discovery and their biological characterization -- 2.4. SNPs2ChIP identifies relevant functions of the non-coding genome -- 2.4.1. Genome-wide SNPs coverage of the reference datasets -- 2.4.2. Non-coding GWAS SNPs of systemic lupus erythematosus -- 2.4.3. ChIP-seq peaks for vitamin D receptors. |
| 505 8# - İÇİNDEKİLER NOTU | |
| İçindekiler Notu | 2.5. Robustness Analysis in the latent factor identification. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Dunker, A Keith. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Hunter, Lawrence. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Ritchie, Marylyn D. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Murray, Tiffany A. |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Klein, Teri E. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Print version: |
| Main entry heading | Altman, Russ B |
| Title | Biocomputing 2019 - Proceedings Of The Pacific Symposium |
| Place, publisher, and date of publication | Singapore : World Scientific Publishing Company,c2018 |
| International Standard Book Number | 9789813279810 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELEKTRONİK YER ve ERİŞİM | |
| Kaynak Tanımlayıcı (URL) | <a href="https://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6383183">https://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6383183</a> |
| Public note | Click to View |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Ekli Tam Metin | Description based on publisher supplied metadata and other sources. |
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