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A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology

Onur Dereli, Ceyda Oguz, Mehmet Gönen
2019 International Conference on Machine Learning  
Rather than performing survival analysis on each data set to predict survival times of cancer patients, we developed a novel multitask approach based on multiple kernel learning (MKL).  ...  Our multitask MKL algorithm both works on multiple cancer data sets and integrates cancer-related pathways/gene sets into survival analysis.  ...  In this study, we combined survival analysis, MKL (for pathway selection) and multitask learning (for modeling multiple cohorts) in a unified formulation for the first time.  ... 
dblp:conf/icml/DereliOG19 fatcat:zzktvfce3ne5nakic4yfpg27x4

Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation

Mehmet Gönen
2016 BMC Bioinformatics  
We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly.  ...  We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes.  ...  To this aim, we propose a Bayesian multiple kernel learning algorithm, which produces a classifier with sparse gene set weights, by extending our earlier Bayesian formulation [8] .  ... 
doi:10.1186/s12859-016-1311-3 pmid:28105911 pmcid:PMC5249028 fatcat:bllzdzxgyzestgfevzus7sw454

PIMKL: Pathway-Induced Multiple Kernel Learning

Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez
2019 npj Systems Biology and Applications  
PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm  ...  We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification.  ...  ACKNOWLEDGEMENTS We thank Yupeng Cun for kindly providing results 13 for the creation of Figs. 1a and S1.  ... 
doi:10.1038/s41540-019-0086-3 pmid:30854223 pmcid:PMC6401099 fatcat:nv7rall7crepdcnk6gwzyqwk3e

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Nupur Biswas, Saikat Chakrabarti
2020 Frontiers in Oncology  
Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the  ...  analysis and understanding of multi-omics data for patient-specific observations.  ...  The authors acknowledge the CSIR-Indian Institute of Chemical Biology for infrastructural support.  ... 
doi:10.3389/fonc.2020.588221 pmid:33154949 pmcid:PMC7591760 fatcat:5kfd6jid6vcx7h4qvk52pngo5m

A community effort to assess and improve drug sensitivity prediction algorithms

James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-ud-din, Petteri Hintsanen, Suleiman A Khan, John-Patrick Mpindi, Olli Kallioniemi (+10 others)
2014 Nature Biotechnology  
a n a ly s i s advance online publication nature biotechnology a n a ly s i s models.  ...  The evolution of the DREAM project will continue as the challenges in biomedical research expand to the genome scale.  ...  Team Synopsis The second set a Original views Bayesian multitask multiple kernel learning Multitask learning Multiple kernel learning Kernels K 1 K K K Kernel weights Kernel 1 Kernel  ... 
doi:10.1038/nbt.2877 pmid:24880487 pmcid:PMC4547623 fatcat:psjjxz2orbga7newjwf7d6bqea

Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping
2019 Genes  
In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing  ...  Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine.  ...  Multiple kernel learning (MKL), a robust integrative analysis approach with heterogenous data, employs different kernels or similarity functions for data from different sources and fuses them into a global  ... 
doi:10.3390/genes10020087 pmid:30696086 pmcid:PMC6410075 fatcat:vopnjgke4fculmr7t3n43ewfiy

Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications

Pan Zheng, Shudong Wang, Xun Wang, Xiangxiang Zeng
2022 Frontiers in Genetics  
Special gratitude and appreciation are extended to all the contributors for their high-quality submissions and the reviewers for volunteering their time and expertise to review the scientific merit of  ...  ACKNOWLEDGMENTS We sincerely appreciate Frontiers in Genetics giving us this opportunity to organize this research topic.  ...  The method uses multitask learning integrated with the bidirectional gated recurrent units (BGRU).  ... 
doi:10.3389/fgene.2022.870795 pmid:35368698 pmcid:PMC8969764 fatcat:vxxdfzq2rnbqboqww6gcxpvema

Reconstructing cancer drug response networks using multitask learning

Matthew Ruffalo, Petar Stojanov, Venkata Krishna Pillutla, Rohan Varma, Ziv Bar-Joseph
2017 BMC Systems Biology  
Translating in vitro results to clinical tests is a major challenge in systems biology.  ...  We used top proteins from each drug network to predict survival for patients prescribed the drug.  ...  Acknowledgements Not applicable.  ... 
doi:10.1186/s12918-017-0471-8 pmid:29017547 pmcid:PMC5635550 fatcat:h3omquo2azb2la56pcu6rcoji4

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge

Vladislav Uzunangelov, Christopher K. Wong, Joshua M. Stuart, Anna R Panchenko
2021 PLoS Computational Biology  
AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates  ...  , survival in breast cancer, and cell line response to gene knockdowns.  ...  Consequently, many bioinformatics approaches seek to combine data at the level of a biological process to benefit machine-learning applications in the cancer genomics setting.  ... 
doi:10.1371/journal.pcbi.1008878 pmid:33861732 fatcat:qo3d2mzfhnftrglo4yhqlsw5dm

Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design [chapter]

John W. Cassidy
2020 Artificial Intelligence in Oncology Drug Discovery and Development  
Machine learning (ML) has re-emerged in the last several years as a powerful set of tools for unlocking value from large datasets.  ...  We focus our analysis on oncology, though make reference to the wider field of human health and disease. Learning in Drug Discovery I: Target Discovery and Small Molecule...  ...  For these reasons, the application of ML to the field of modern biology is extremely well suited.  ... 
doi:10.5772/intechopen.93159 fatcat:z5xuzsnga5ggfnp5sofk6hyodi

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Babak Arjmand, Shayesteh Kokabi Hamidpour, Akram Tayanloo-Beik, Parisa Goodarzi, Hamid Reza Aghayan, Hossein Adibi, Bagher Larijani
2022 Frontiers in Genetics  
In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics  ...  Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms.  ...  Multiple imputation; MICE, multivariate imputation by chained equation; MIDA, denoising autoencoder-based MI; MI-MFA, MI for multiple factor analysis; MKL, Multiple kernel learning; ML, Machine learning  ... 
doi:10.3389/fgene.2022.824451 pmid:35154283 pmcid:PMC8829119 fatcat:n5infqv5ifgeveaksrtdco4jui

Cascaded Wx: A Novel Prognosis-Related Feature Selection Framework in Human Lung Adenocarcinoma Transcriptomes

Bonggun Shin, Sungsoo Park, Ji Hyung Hong, Ho Jung An, Sang Hoon Chun, Kilsoo Kang, Young-Ho Ahn, Yoon Ho Ko, Keunsoo Kang
2019 Frontiers in Genetics  
Artificial neural network-based analysis has recently been used to predict clinical outcomes in patients with solid cancers, including lung cancer.  ...  The CWx framework ranks features according to the survival of a given cohort by training neural networks with three different high- and low-risk groups in a cascaded fashion.  ...  Machine learning (ML) algorithms can be a useful approach to the analysis of high volumes of data if a model is well constructed with high-quality input data for training.  ... 
doi:10.3389/fgene.2019.00662 pmid:31379926 pmcid:PMC6658675 fatcat:cnssawo2xfgpnfr26sqgfruqwi

Applications of machine learning in drug discovery and development

Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, Shanrong Zhao
2019 Nature reviews. Drug discovery  
Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data.  ...  With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential  ...  Papa for helpful comments, M. Segler for contributing to the small-molecule optimization subsection and A. Janowczyk for providing the pathology images in Figure 4 .  ... 
doi:10.1038/s41573-019-0024-5 pmid:30976107 pmcid:PMC6552674 fatcat:4gubtr5kz5fe7khrbtfrre4lhy

Structural and functional radiomics for lung cancer

Guangyao Wu, Arthur Jochems, Turkey Refaee, Abdalla Ibrahim, Chenggong Yan, Sebastian Sanduleanu, Henry C. Woodruff, Philippe Lambin
2021 European Journal of Nuclear Medicine and Molecular Imaging  
Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.  ...  Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer  ...  Radiomics refers to the extraction and analysis of quantitative image features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes  ... 
doi:10.1007/s00259-021-05242-1 pmid:33693966 pmcid:PMC8484174 fatcat:obo4bghkp5gx3a52ibiuvq22hq

An overview of machine learning methods for monotherapy drug response prediction

Farzaneh Firoozbakht, Behnam Yousefi, Benno Schwikowski
2021 Briefings in Bioinformatics  
Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses  ...  ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it.  ...  Acknowledgements We thank the anonymous reviewers and Krister Wennerberg for helpful comments on a previous version of this article.  ... 
doi:10.1093/bib/bbab408 pmid:34619752 pmcid:PMC8769705 fatcat:t4gwewnsarb5rm6tx3zdbqnhhm
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