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Bioinformatics and Machine Learning for Cancer Biology
| Bioinformatics and Machine Learning for Cancer Biology |
| Autore | Wan Shibiao |
| Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
| Descrizione fisica | 1 electronic resource (196 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences |
| Soggetto non controllato |
tumor mutational burden
DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595077403321 |
Wan Shibiao
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| Basel, : MDPI Books, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak
| Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak |
| Autore | Wan Shibiao |
| Pubbl/distr/stampa | Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina | 572/.696 |
| Soggetto topico |
Proteins - Physiological transport - Data processing
Machine learning Probabilities - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-5015-0150-X
1-5015-0152-6 |
| Classificazione | WC 7700 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
| Record Nr. | UNINA-9910460442103321 |
|
Wan Shibiao
|
||
| Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak
| Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak |
| Autore | Wan Shibiao |
| Pubbl/distr/stampa | Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina | 572/.696 |
| Soggetto topico |
Proteins - Physiological transport - Data processing
Machine learning Probabilities - Data processing |
| Soggetto non controllato |
Bioinformatics
Computer Science Proteomics |
| ISBN |
1-5015-0150-X
1-5015-0152-6 |
| Classificazione | WC 7700 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
| Record Nr. | UNINA-9910797139603321 |
|
Wan Shibiao
|
||
| Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak
| Machine learning for protein subcellular localization prediction / / Shibiao Wan, Man-Wai Mak |
| Autore | Wan Shibiao |
| Pubbl/distr/stampa | Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina | 572/.696 |
| Soggetto topico |
Proteins - Physiological transport - Data processing
Machine learning Probabilities - Data processing |
| Soggetto non controllato |
Bioinformatics
Computer Science Proteomics |
| ISBN |
1-5015-0150-X
1-5015-0152-6 |
| Classificazione | WC 7700 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Preface -- Contents -- List of Abbreviations -- 1. Introduction -- 2. Overview of subcellular localization prediction -- 3. Legitimacy of using gene ontology information -- 4. Single-location protein subcellular localization -- 5. From single- to multi-location -- 6. Mining deeper on GO for protein subcellular localization -- 7. Ensemble random projection for large-scale predictions -- 8. Experimental setup -- 9. Results and analysis -- 10. Properties of the proposed predictors -- 11. Conclusions and future directions -- A. Webservers for protein subcellular localization -- B. Support vector machines -- C. Proof of no bias in LOOCV -- D. Derivatives for penalized logistic regression -- Bibliography -- Index |
| Record Nr. | UNINA-9910819391103321 |
|
Wan Shibiao
|
||
| Berlin, Germany ; ; Boston, Massachusetts : , : De Gruyter, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||