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Steyaert S, Pizurica M, Nagaraj D, Khandelwal P, Hernandez-Boussard T, Gentles AJ, Gevaert O. Multimodal data fusion for cancer biomarker discovery with deep learning. NAT MACH INTELL 2023; 5:351-362. [PMID: 37693852 PMCID: PMC10484010 DOI: 10.1038/s42256-023-00633-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/17/2023] [Indexed: 09/12/2023]
Abstract
Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.
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Affiliation(s)
- Sandra Steyaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | | | | | - Tina Hernandez-Boussard
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
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2
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Ng HM, Jiang B, Wong KY. Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis. Biom J 2023; 65:e2100139. [PMID: 35837982 DOI: 10.1002/bimj.202100139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 04/15/2022] [Accepted: 05/27/2022] [Indexed: 01/17/2023]
Abstract
Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.
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Affiliation(s)
- Hoi Min Ng
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
| | - Binyan Jiang
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
| | - Kin Yau Wong
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
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3
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Li P, Luo H, Ji B, Nielsen J. Machine learning for data integration in human gut microbiome. Microb Cell Fact 2022; 21:241. [PMID: 36419034 PMCID: PMC9685977 DOI: 10.1186/s12934-022-01973-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 11/15/2022] [Indexed: 11/25/2022] Open
Abstract
Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.
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Affiliation(s)
- Peishun Li
- grid.5371.00000 0001 0775 6028Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Hao Luo
- grid.5371.00000 0001 0775 6028Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Boyang Ji
- grid.5371.00000 0001 0775 6028Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden ,grid.510909.4BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark
| | - Jens Nielsen
- grid.5371.00000 0001 0775 6028Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden ,grid.510909.4BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark
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4
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Carrillo-Perez F, Morales JC, Castillo-Secilla D, Gevaert O, Rojas I, Herrera LJ. Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis. J Pers Med 2022; 12:601. [PMID: 35455716 PMCID: PMC9025878 DOI: 10.3390/jpm12040601] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 03/29/2022] [Accepted: 04/06/2022] [Indexed: 01/27/2023] Open
Abstract
Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.
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Affiliation(s)
- Francisco Carrillo-Perez
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18170 Granada, Spain; (J.C.M.); (I.R.); (L.J.H.)
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, 1265 Welch Rd, Stanford, CA 94305, USA;
| | - Juan Carlos Morales
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18170 Granada, Spain; (J.C.M.); (I.R.); (L.J.H.)
| | - Daniel Castillo-Secilla
- Fujitsu Technology Solutions S.A, CoE Data Intelligence, Camino del Cerro de los Gamos, 1, Pozuelo de Alarcón, 28224 Madrid, Spain;
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, 1265 Welch Rd, Stanford, CA 94305, USA;
| | - Ignacio Rojas
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18170 Granada, Spain; (J.C.M.); (I.R.); (L.J.H.)
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18170 Granada, Spain; (J.C.M.); (I.R.); (L.J.H.)
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5
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Duan R, Gao L, Gao Y, Hu Y, Xu H, Huang M, Song K, Wang H, Dong Y, Jiang C, Zhang C, Jia S. Evaluation and comparison of multi-omics data integration methods for cancer subtyping. PLoS Comput Biol 2021; 17:e1009224. [PMID: 34383739 PMCID: PMC8384175 DOI: 10.1371/journal.pcbi.1009224] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 08/24/2021] [Accepted: 06/28/2021] [Indexed: 11/18/2022] Open
Abstract
Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis. Cancer is one of the most heterogeneous diseases, characterized by diverse morphological, phenotypic, and genomic profiles between tumors and their subtypes. Identifying cancer subtypes can help patients receive precise treatments. With the development of high-throughput technologies, genomics, epigenomics, and transcriptomics data have been generated for large cancer patient cohorts. It is believed that the more omics data we use, the more accurate identification of cancer subtypes. To examine this assumption, we first constructed three classes of benchmarking datasets to conduct a comprehensive evaluation and comparison of ten representative multi-omics data integration methods for cancer subtyping by considering their accuracy, robustness, and computational efficiency. Then, we investigated the influence of different omics data and their various combinations on the effectiveness of cancer subtyping. Our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. We hope that our work may help researchers choose a proper method and an effective data combination when identifying cancer subtypes using data integration methods.
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Affiliation(s)
- Ran Duan
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an, China
- * E-mail:
| | - Yong Gao
- Department of Computer Science, The University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Han Xu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Mingfeng Huang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Kuo Song
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Hongda Wang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Yongqiang Dong
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Chaoqun Jiang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Chenxing Zhang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Songwei Jia
- School of Computer Science and Technology, Xidian University, Xi’an, China
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6
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Jendoubi T. Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer. Metabolites 2021; 11:184. [PMID: 33801081 PMCID: PMC8003953 DOI: 10.3390/metabo11030184] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.
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Affiliation(s)
- Takoua Jendoubi
- Department of Statistical Science, University College London, London WC1E 6BT, UK
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7
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Maity AK, Lee SC, Mallick BK, Sarkar TR. Bayesian structural equation modeling in multiple omics data with application to circadian genes. Bioinformatics 2020; 36:3951-3958. [PMID: 32369552 DOI: 10.1093/bioinformatics/btaa286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/30/2020] [Accepted: 04/27/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. RESULTS Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. AVAILABILITY AND IMPLEMENTATION The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arnab Kumar Maity
- Early Clinical Development Oncology Statistics, Pfizer Inc., San Diego, CA 92121, USA
| | | | | | - Tapasree Roy Sarkar
- Department of Statistics.,Department of Biology, Texas A&M University, College Station, TX 77843, USA
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8
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A survey on single and multi omics data mining methods in cancer data classification. J Biomed Inform 2020; 107:103466. [DOI: 10.1016/j.jbi.2020.103466] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 05/01/2020] [Accepted: 05/31/2020] [Indexed: 01/09/2023]
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9
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Tini G, Marchetti L, Priami C, Scott-Boyer MP. Multi-omics integration-a comparison of unsupervised clustering methodologies. Brief Bioinform 2020; 20:1269-1279. [PMID: 29272335 DOI: 10.1093/bib/bbx167] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 11/06/2017] [Indexed: 12/19/2022] Open
Abstract
With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.
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10
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Oh M, Park S, Kim S, Chae H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief Bioinform 2020; 22:66-76. [PMID: 32227074 DOI: 10.1093/bib/bbaa032] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 02/05/2020] [Accepted: 02/25/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.
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Affiliation(s)
- Minsik Oh
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.,Bioinformatics Institute, Seoul National University, Seoul, 08826, Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women's University, Seoul, 04310,Korea
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11
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Jiang D, Armour CR, Hu C, Mei M, Tian C, Sharpton TJ, Jiang Y. Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities. Front Genet 2019; 10:995. [PMID: 31781153 PMCID: PMC6857202 DOI: 10.3389/fgene.2019.00995] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 09/18/2019] [Indexed: 12/21/2022] Open
Abstract
The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.
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Affiliation(s)
- Duo Jiang
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Courtney R Armour
- Department of Microbiology, Oregon State University, Corvallis, OR, United States
| | - Chenxiao Hu
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Meng Mei
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Chuan Tian
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Thomas J Sharpton
- Department of Statistics, Oregon State University, Corvallis, OR, United States
- Department of Microbiology, Oregon State University, Corvallis, OR, United States
| | - Yuan Jiang
- Department of Statistics, Oregon State University, Corvallis, OR, United States
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12
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Zhang Y, Morris JS, Aerry SN, Rao AU, Baladandayuthapani V. RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA. Ann Appl Stat 2019; 13:1957-1988. [PMID: 33224404 PMCID: PMC7678720 DOI: 10.1214/19-aoas1238] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, wherein our goals are to simultaneously identify genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose Radio-iBAG: Radiomics-based Integrative Bayesian Analysis of Multiplatform Genomic Data, a multi-scale Bayesian hierarchical model that involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets to capture fundamental biological relationships; explores the associations between radiomic markers accompanying genomic information with clinical outcomes; and detects genomic and radiomic markers associated with clinical prognosis. We also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse set of approximately orthogonal meta-features each containing information from a set of related individual radiomic features, reducing dimensionality and combining like features. Our methods are motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, where-in we integrate magnetic resonance imaging-based biomarkers along with genomic, epigenomic and transcriptomic data. Our model identifies important magnetic resonance imaging features and the associated genomic platforms that are related with patient survival times.
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Affiliation(s)
- Youyi Zhang
- The University of Texas MD Anderson Cancer Center
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13
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Horizontal and vertical integrative analysis methods for mental disorders omics data. Sci Rep 2019; 9:13430. [PMID: 31530853 PMCID: PMC6748966 DOI: 10.1038/s41598-019-49718-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 08/30/2019] [Indexed: 12/18/2022] Open
Abstract
In recent biomedical studies, omics profiling has been extensively conducted on various types of mental disorders. In most of the existing analyses, a single type of mental disorder and a single type of omics measurement are analyzed. In the study of other complex diseases, integrative analysis, both vertical and horizontal integration, has been conducted and shown to bring significantly new insights into disease etiology, progression, biomarkers, and treatment. In this article, we showcase the applicability of integrative analysis to mental disorders. In particular, the horizontal integration of bipolar disorder and schizophrenia and the vertical integration of gene expression and copy number variation data are conducted. The analysis is based on the sparse principal component analysis, penalization, and other advanced statistical techniques. In data analysis, integration leads to biologically sensible findings, including the disease-related gene expressions, copy number variations, and their associations, which differ from the “benchmark” analysis. Overall, this study suggests the potential of integrative analysis in mental disorder research.
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14
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Maity AK, Bhattacharya A, Mallick BK, Baladandayuthapani V. Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data. Biometrics 2019; 76:316-325. [PMID: 31393003 DOI: 10.1111/biom.13132] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 07/19/2019] [Indexed: 12/20/2022]
Abstract
Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.
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Affiliation(s)
- Arnab Kumar Maity
- Early Clinical Development Oncology Statistics, Pfizer Inc., San Diego, California
| | | | - Bani K Mallick
- Department of Statistics, Texas A&M University, College Station, Texas
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15
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Abstract
MOTIVATION Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type-using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs. RESULTS We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients. AVAILABILITY AND IMPLEMENTATION https://github.com/gevaertlab/MultimodalPrognosis.
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Affiliation(s)
| | - Olivier Gevaert
- Department of Medicine and Biomedical Data Science, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
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16
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Decision Support Systems in Prostate Cancer Treatment: An Overview. BIOMED RESEARCH INTERNATIONAL 2019; 2019:4961768. [PMID: 31281840 PMCID: PMC6590598 DOI: 10.1155/2019/4961768] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 04/02/2019] [Accepted: 05/06/2019] [Indexed: 12/11/2022]
Abstract
Background A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. Methods We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals. Results Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS. Conclusions Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.
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17
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Fritzius T, Bettler B. The organizing principle of GABA B receptor complexes: Physiological and pharmacological implications. Basic Clin Pharmacol Toxicol 2019; 126 Suppl 6:25-34. [PMID: 31033219 PMCID: PMC7317483 DOI: 10.1111/bcpt.13241] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/15/2019] [Indexed: 12/19/2022]
Abstract
GABAB receptors (GBRs), the G protein-coupled receptors for the neurotransmitter γ-aminobutyric acid (GABA), regulate synaptic transmission at most synapses in the brain. Proteomic approaches revealed that native GBR complexes assemble from an inventory of ~30 proteins that provide a molecular basis for the functional diversity observed with these receptors. Studies with reconstituted GBR complexes in heterologous cells and complementary knockout studies have allowed to identify cellular and physiological functions for obligate and several non-obligate receptor components. It emerges that modular association of receptor components in space and time generates a variety of multiprotein receptor complexes with different localizations, kinetic properties and effector channels. This article summarizes current knowledge on the organizing principle of GBR complexes. We further discuss unanticipated receptor functions, links to disease and opportunities for drug discovery arising from the identification of novel receptor components.
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Affiliation(s)
- Thorsten Fritzius
- Department of Biomedicine, Institute of Physiology, University of Basel, Basel, Switzerland
| | - Bernhard Bettler
- Department of Biomedicine, Institute of Physiology, University of Basel, Basel, Switzerland
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18
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Xu Y. Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:1540-1550. [PMID: 27116760 DOI: 10.1109/tcyb.2016.2551735] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Twin support vector machine (TSVM) finds two nonparallel planes by solving a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the conventional support vector machine (SVM); this makes the learning speed of TSVM approximately four times faster than that of the standard SVM. One major limitation of TSVM is that it involves an expensive matrix inverse operation when solving the dual problem. In addition, TSVM is less effective when dealing with the imbalanced data. In this paper, we propose a maximum margin of twin spheres support vector machine (MMTSSVM) for imbalanced data classification. MMTSSVM only needs to find two homocentric spheres. On one hand, the small sphere captures as many samples in the majority class as possible; on the other hand, the large sphere pushes out most samples in the minority class by increasing the margin between two homocentric spheres. MMTSSVM involves a QPP and a linear programming problem as opposed to a pair of QPPs as in classical TSVM or a larger-sized QPP in SVM, thus it greatly increases the computational speed. More importantly, MMTSSVM avoids the matrix inverse operation. The property of parameters in MMTSSVM is discussed and testified by one artificial experiment. Experimental results on nine benchmark datasets demonstrate the effectiveness of the proposed MMTSSVM in comparison with state-of-the-art algorithms. Finally, we apply MMTSSVM into Alzheimer's disease medical experiment and also obtain a better experimental result.
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19
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Ray B, Ghedin E, Chunara R. Network inference from multimodal data: A review of approaches from infectious disease transmission. J Biomed Inform 2016; 64:44-54. [PMID: 27612975 PMCID: PMC7106161 DOI: 10.1016/j.jbi.2016.09.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 07/10/2016] [Accepted: 09/03/2016] [Indexed: 02/02/2023]
Abstract
Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.
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Affiliation(s)
- Bisakha Ray
- Center for Health Informatics and Bioinformatics, New York University School of Medicine, USA.
| | - Elodie Ghedin
- Department of Biology, Center for Genomics & Systems Biology, USA; College of Global Public Health, New York University, USA
| | - Rumi Chunara
- Dept. of Computer Science and Engineering, Tandon School of Engineering, USA; College of Global Public Health, New York University, USA
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20
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Brown SA. Patient Similarity: Emerging Concepts in Systems and Precision Medicine. Front Physiol 2016; 7:561. [PMID: 27932992 PMCID: PMC5121278 DOI: 10.3389/fphys.2016.00561] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 11/07/2016] [Indexed: 12/19/2022] Open
Affiliation(s)
- Sherry-Ann Brown
- Department of Cardiovascular Diseases, Mayo Clinic Rochester, MN, USA
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21
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Zhu R, Zhao Q, Zhao H, Ma S. Integrating multidimensional omics data for cancer outcome. Biostatistics 2016; 17:605-18. [PMID: 26980320 PMCID: PMC5031941 DOI: 10.1093/biostatistics/kxw010] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 01/27/2016] [Indexed: 01/06/2023] Open
Abstract
In multidimensional cancer omics studies, one subject is profiled on multiple layers of omics activities. In this article, the goal is to integrate multiple types of omics measurements, identify markers, and build a model for cancer outcome. The proposed analysis is achieved in two steps. In the first step, we analyze the regulation among different types of omics measurements, through the construction of linear regulatory modules (LRMs). The LRMs have sound biological basis, and their construction differs from the existing analyses by modeling the regulation of sets of gene expressions (GEs) by sets of regulators. The construction is realized with the assistance of regularized singular value decomposition. In the second step, the proposed cancer outcome model includes the regulated GEs, "residuals" of GEs, and "residuals" of regulators, and we use regularized estimation to select relevant markers. Simulation shows that the proposed method outperforms the alternatives with more accurate marker identification. We analyze the The Cancer Genome Atlas data on cutaneous melanoma and lung adenocarcinoma and obtain meaningful results.
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Affiliation(s)
- Ruoqing Zhu
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Qing Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Shuangge Ma
- Department of Biostatistics, Yale University, New Haven, CT, USA
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22
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Xu Y, Yang Z, Zhang Y, Pan X, Wang L. A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2015.12.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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23
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Savage RS, Yuan Y. Predicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusion. ROYAL SOCIETY OPEN SCIENCE 2016; 3:140501. [PMID: 26998311 PMCID: PMC4785962 DOI: 10.1098/rsos.140501] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 01/08/2016] [Indexed: 05/30/2023]
Abstract
Predicting response to treatment and disease-specific deaths are key tasks in cancer research yet there is a lack of methodologies to achieve these. Large-scale 'omics and digital pathology technologies have led to the need for effective statistical methods for data fusion to extract the most useful patterns from these diverse data types. We present FusionGP, a method for combining heterogeneous data types designed specifically for predicting outcome of treatment and disease. FusionGP is a Gaussian process model that includes a generalization of feature selection for biomarker discovery, allowing for simultaneous, sparse feature selection across multiple data types. Importantly, it can accommodate highly nonlinear structure in the data, and automatically infers the optimal contribution from each input data type. FusionGP compares favourably to several popular classification methods, including the Random Forest classifier, a stepwise logistic regression model and the Support Vector Machine on single data types. By combining gene expression, copy number alteration and digital pathology image data in 119 estrogen receptor (ER)-negative and 345 ER-positive breast tumours, we aim to predict two important clinical outcomes: death and chemoinsensitivity. While gene expression data give the best predictive performance in the majority of cases, the digital pathology data are much better for predicting death in ER cases. Thus, FusionGP is a new tool for selecting informative features from heterogeneous data types and predicting treatment response and prognosis.
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Affiliation(s)
- Richard S. Savage
- Systems Biology Centre, University of Warwick, Warwick, UK
- Warwick Medical School, University of Warwick, Warwick, UK
| | - Yinyin Yuan
- Division of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
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24
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Scheurwegs E, Luyckx K, Luyten L, Daelemans W, Van den Bulcke T. Data integration of structured and unstructured sources for assigning clinical codes to patient stays. J Am Med Inform Assoc 2015; 23:e11-9. [PMID: 26316458 DOI: 10.1093/jamia/ocv115] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 06/29/2015] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE Enormous amounts of healthcare data are becoming increasingly accessible through the large-scale adoption of electronic health records. In this work, structured and unstructured (textual) data are combined to assign clinical diagnostic and procedural codes (specifically ICD-9-CM) to patient stays. We investigate whether integrating these heterogeneous data types improves prediction strength compared to using the data types in isolation. METHODS Two separate data integration approaches were evaluated. Early data integration combines features of several sources within a single model, and late data integration learns a separate model per data source and combines these predictions with a meta-learner. This is evaluated on data sources and clinical codes from a broad set of medical specialties. RESULTS When compared with the best individual prediction source, late data integration leads to improvements in predictive power (eg, overall F-measure increased from 30.6% to 38.3% for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes), while early data integration is less consistent. The predictive strength strongly differs between medical specialties, both for ICD-9-CM diagnostic and procedural codes. DISCUSSION Structured data provides complementary information to unstructured data (and vice versa) for predicting ICD-9-CM codes. This can be captured most effectively by the proposed late data integration approach. CONCLUSIONS We demonstrated that models using multiple electronic health record data sources systematically outperform models using data sources in isolation in the task of predicting ICD-9-CM codes over a broad range of medical specialties.
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Affiliation(s)
- Elyne Scheurwegs
- ADReM (Advanced Database Research and Modelling), Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Kim Luyckx
- Department of Medical Information, Antwerp University Hospital, Antwerp, Belgium
| | - Léon Luyten
- Department of Medical Information, Antwerp University Hospital, Antwerp, Belgium
| | - Walter Daelemans
- Computational Linguistics and Psycholinguistics (CLiPS) Research Center, University of Antwerp, Antwerp, Belgium
| | - Tim Van den Bulcke
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp - Antwerp University Hospital, Belgium; ADReM (Advanced Database Research and Modelling), University of Antwerp, Antwerp, Belgium
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25
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26
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Chekouo T, Stingo FC, Doecke JD, Do KA. miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer. Biometrics 2015; 71:428-38. [PMID: 25639276 DOI: 10.1111/biom.12266] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Revised: 09/01/2014] [Accepted: 10/01/2014] [Indexed: 11/30/2022]
Abstract
The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. We employ non-local prior distributions and a stochastic search method for the selection of biomarkers associated with the survival outcome. We use KEGG pathway information to incorporate correlated gene effects within regulatory networks. Using simulation studies, we assess the performance of our method, and apply it to experimental data of kidney renal cell carcinoma (KIRC) obtained from The Cancer Genome Atlas. Our novel method validates previously identified cancer biomarkers and identifies biomarkers specific to KIRC progression that were not previously discovered. Using the KIRC data, we confirm that biomarkers involved in regulatory networks are more likely to be associated with survival time, showing connections in one regulatory network for five out of six such genes we identified.
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Affiliation(s)
- Thierry Chekouo
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722, U.S.A
| | - Francesco C Stingo
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722, U.S.A
| | - James D Doecke
- CSIRO Computational Informatics/Australian e-Health Research Centre Level 5, UQ Health Sciences Building, 901/16 Royal Brisbane, Queensland, 4029, Australia
| | - Kim-Anh Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722, U.S.A
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27
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Thomas M, De Brabanter K, Suykens JAK, De Moor B. Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinformatics 2014; 15:411. [PMID: 25551433 PMCID: PMC4308909 DOI: 10.1186/s12859-014-0411-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 12/05/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. RESULTS We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. CONCLUSIONS Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.
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Affiliation(s)
- Minta Thomas
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
| | - Kris De Brabanter
- Department of Statistics & Computer Science, Iowa State University, Ames, IA, USA.
| | - Johan A K Suykens
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
| | - Bart De Moor
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
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Gregory KB, Momin AA, Coombes KR, Baladandayuthapani V. Latent Feature Decompositions for Integrative Analysis of Multi-Platform Genomic Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:984-994. [PMID: 26146492 PMCID: PMC4486317 DOI: 10.1109/tcbb.2014.2325035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to a glioblastoma multiforme data set from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features.
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Affiliation(s)
- Karl B. Gregory
- PhD candidate in Department of Statistics, Texas A&M University, College Station, TX, 77843-3143, USA
| | - Amin A. Momin
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, 77230-1402, USA
| | - Kevin R. Coombes
- Department of Biomedical Informatics, The Ohio State University Wexner Medical Center, USA
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Jayawardana K, Schramm SJ, Haydu L, Thompson JF, Scolyer RA, Mann GJ, Müller S, Yang JYH. Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. Int J Cancer 2014; 136:863-74. [DOI: 10.1002/ijc.29047] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2014] [Accepted: 06/06/2014] [Indexed: 01/19/2023]
Affiliation(s)
- Kaushala Jayawardana
- School of Mathematics & Statistics; The University of Sydney; Sydney NSW Australia
| | - Sarah-Jane Schramm
- Sydney Medical School; The University of Sydney at Westmead Millennium Institute for Medical Research; Westmead NSW Australia
- Melanoma Institute Australia; Sydney NSW Australia
| | - Lauren Haydu
- Melanoma Institute Australia; Sydney NSW Australia
| | - John F. Thompson
- Melanoma Institute Australia; Sydney NSW Australia
- Discipline of Surgery; The University of Sydney; Sydney NSW Australia
| | - Richard A. Scolyer
- Melanoma Institute Australia; Sydney NSW Australia
- Discipline of Pathology; The University of Sydney; Sydney NSW Australia
- Tissue Pathology and Diagnostic Oncology; Royal Prince Alfred Hospital; Camperdown NSW Australia
| | - Graham J. Mann
- Sydney Medical School; The University of Sydney at Westmead Millennium Institute for Medical Research; Westmead NSW Australia
- Melanoma Institute Australia; Sydney NSW Australia
| | - Samuel Müller
- School of Mathematics & Statistics; The University of Sydney; Sydney NSW Australia
| | - Jean Yee Hwa Yang
- School of Mathematics & Statistics; The University of Sydney; Sydney NSW Australia
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30
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Thomas M, Daemen A, De Moor B. Maximum Likelihood Estimation of GEVD: Applications in Bioinformatics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:673-680. [PMID: 26356338 DOI: 10.1109/tcbb.2014.2304292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose a method, maximum likelihood estimation of generalized eigenvalue decomposition (MLGEVD) that employs a well known technique relying on the generalization of singular value decomposition (SVD). The main aim of the work is to show the tight equivalence between MLGEVD and generalized ridge regression. This relationship reveals an important mathematical property of GEVD in which the second argument act as prior information in the model. Thus we show that MLGEVD allows the incorporation of external knowledge about the quantities of interest into the estimation problem. We illustrate the importance of prior knowledge in clinical decision making/identifying differentially expressed genes with case studies for which microarray data sets with corresponding clinical/literature information are available. On all of these three case studies, MLGEVD outperformed GEVD on prediction in terms of test area under the ROC curve (test AUC). MLGEVD results in significantly improved diagnosis, prognosis and prediction of therapy response.
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31
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Ray B, Henaff M, Ma S, Efstathiadis E, Peskin ER, Picone M, Poli T, Aliferis CF, Statnikov A. Information content and analysis methods for multi-modal high-throughput biomedical data. Sci Rep 2014; 4:4411. [PMID: 24651673 PMCID: PMC3961740 DOI: 10.1038/srep04411] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 02/27/2014] [Indexed: 01/30/2023] Open
Abstract
The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect "multi-modal" data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.
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Affiliation(s)
- Bisakha Ray
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
| | - Mikael Henaff
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
- Department of Computer Science, New York University, NY, USA
| | - Sisi Ma
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
| | - Efstratios Efstathiadis
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
| | - Eric R. Peskin
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
| | - Marco Picone
- Department of Information Engineering, University of Parma, Parma, Italy
- MultiMed Srl, Cremona, Italy
| | - Tito Poli
- Maxillofacial Surgery Section of the Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Constantin F. Aliferis
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
- Department of Pathology, New York University School of Medicine, New York, NY, USA
| | - Alexander Statnikov
- Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, USA
- Department of Medicine, New York University School of Medicine, New York, NY, USA
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Zakeri P, Jeuris B, Vandebril R, Moreau Y. Protein fold recognition using geometric kernel data fusion. ACTA ACUST UNITED AC 2014; 30:1850-7. [PMID: 24590441 PMCID: PMC4071197 DOI: 10.1093/bioinformatics/btu118] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Motivation: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. Results: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. Availability and implementation: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/ Contact:pooyapaydar@gmail.com or yves.moreau@esat.kuleuven.be Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pooya Zakeri
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, BelgiumDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
| | - Ben Jeuris
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
| | - Raf Vandebril
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
| | - Yves Moreau
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, BelgiumDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, BelgiumDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, iMinds Medical IT and Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
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Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do KA. iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. ACTA ACUST UNITED AC 2012; 29:149-59. [PMID: 23142963 PMCID: PMC3546799 DOI: 10.1093/bioinformatics/bts655] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Motivation: Analyzing data from multi-platform genomics experiments combined
with patients’ clinical outcomes helps us understand the complex biological
processes that characterize a disease, as well as how these processes relate to the
development of the disease. Current data integration approaches are limited in that they
do not consider the fundamental biological relationships that exist among the data
obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics
data (iBAG) framework for identifying important genes/biomarkers that are associated with
clinical outcome. This framework uses hierarchical modeling to combine the data obtained
from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic
and real examples. Simulations show our integrative methods to have higher power to detect
disease-related genes than non-integrative methods. Using the Cancer Genome Atlas
glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation
data to study their associations with patient survival. Our proposed method discovers
multiple methylation-regulated genes that are related to patient survival, most of which
have important biological functions in other diseases but have not been previously studied
in glioblastoma. Availability:http://odin.mdacc.tmc.edu/∼vbaladan/. Contact:veera@mdanderson.org Supplementary information:Supplementary data are available at Bioinformatics
online.
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Affiliation(s)
- Wenting Wang
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA
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Lussier YA, Li H. Breakthroughs in genomics data integration for predicting clinical outcome. J Biomed Inform 2012; 45:1199-201. [PMID: 23117078 DOI: 10.1016/j.jbi.2012.10.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Revised: 10/15/2012] [Accepted: 10/17/2012] [Indexed: 11/24/2022]
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Van Gorp T, Cadron I, Daemen A, De Moor B, Waelkens E, Vergote I. Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS. Proteome Sci 2012; 10:41. [PMID: 22694804 PMCID: PMC3493309 DOI: 10.1186/1477-5956-10-41] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2011] [Accepted: 05/25/2012] [Indexed: 11/10/2022] Open
Abstract
UNLABELLED BACKGROUND Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling. MATERIAL & METHODS Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease. RESULTS LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC) of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81), to predict recurrence (AUC 0.92), and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88), between squamous and adenosquamous carcinomas (AUC 0.85), and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94). CONCLUSIONS Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.
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Affiliation(s)
- Toon Van Gorp
- Department of Obstetrics and Gynaecology, Leuven Cancer Institute, Universitaire Ziekenhuizen Leuven, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
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Pahikkala T, Okser S, Airola A, Salakoski T, Aittokallio T. Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations. Algorithms Mol Biol 2012; 7:11. [PMID: 22551170 PMCID: PMC3606421 DOI: 10.1186/1748-7188-7-11] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2011] [Accepted: 04/23/2012] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have analyzed single variants one at a time, it has been proposed that the genetic basis of these disorders could be determined through detailed analysis of the genetic variants themselves and in conjunction with one another. The construction of models that account for these subsets of variants requires methodologies that generate predictions based on the total risk of a particular group of polymorphisms. However, due to the excessive number of variants, constructing these types of models has so far been computationally infeasible. RESULTS We have implemented an algorithm, known as greedy RLS, that we use to perform the first known wrapper-based feature selection on the genome-wide level. The running time of greedy RLS grows linearly in the number of training examples, the number of features in the original data set, and the number of selected features. This speed is achieved through computational short-cuts based on matrix calculus. Since the memory consumption in present-day computers can form an even tighter bottleneck than running time, we also developed a space efficient variation of greedy RLS which trades running time for memory. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm. As a proof of concept, we apply greedy RLS to the Hypertension - UK National Blood Service WTCCC dataset and select the most predictive variants using 3-fold external cross-validation in less than 26 minutes on a high-end desktop. On this dataset, we also show that greedy RLS has a better classification performance on independent test data than a classifier trained using features selected by a statistical p-value-based filter, which is currently the most popular approach for constructing predictive models in GWAS. CONCLUSIONS Greedy RLS is the first known implementation of a machine learning based method with the capability to conduct a wrapper-based feature selection on an entire GWAS containing several thousand examples and over 400,000 variants. In our experiments, greedy RLS selected a highly predictive subset of genetic variants in a fraction of the time spent by wrapper-based selection methods used together with SVM classifiers. The proposed algorithms are freely available as part of the RLScore software library at http://users.utu.fi/aatapa/RLScore/.
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Affiliation(s)
- Tapio Pahikkala
- Department of Information Technology, University of Turku, Turku, Finland
- Turku Centre for Computer Science, Turku, Finland
| | - Sebastian Okser
- Department of Information Technology, University of Turku, Turku, Finland
- Turku Centre for Computer Science, Turku, Finland
| | - Antti Airola
- Department of Information Technology, University of Turku, Turku, Finland
- Turku Centre for Computer Science, Turku, Finland
| | - Tapio Salakoski
- Department of Information Technology, University of Turku, Turku, Finland
- Turku Centre for Computer Science, Turku, Finland
| | - Tero Aittokallio
- Turku Centre for Computer Science, Turku, Finland
- Department of Mathematics, University of Turku, Turku, Finland
- Data Mining and Modeling group, Turku Centre for Biotechnology, Turku, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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Noviyanto A, Wasito I. Evaluation of data integration strategies based on kernel method of clinical and microarray data. Bioinformation 2012; 8:147-50. [PMID: 22368387 PMCID: PMC3283887 DOI: 10.6026/97320630008147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2012] [Accepted: 01/20/2012] [Indexed: 11/23/2022] Open
Abstract
The cancer classification problem is one of the most challenging problems in bioinformatics. The data provided by Netherland Cancer Institute consists of 295 breast cancer patient; 101 patients are with distant metastases and 194 patients are without distant metastases. Combination of features sets based on kernel method to classify the patient who are with or without distant metastases will be investigated. The single data set will be compared with three data integration strategies and also weighted data integration strategies based on kernel method. Least Square Support Vector Machine (LS-SVM) is chosen as the classifier because it can handle very high dimensional features, for instance, microarray data. The experiment result shows that the performance of weighted late integration and the using of only microarray data are almost similar. The data integration strategy is not always better than using single data set in this case. The performance of classification absolutely depends on the features that are used to represent the object.
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Affiliation(s)
| | - Ito Wasito
- Faculty of Computer Science, Universitas Indonesia
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Abstract
Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.
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Gade S, Porzelius C, Fälth M, Brase JC, Wuttig D, Kuner R, Binder H, Sültmann H, Beissbarth T. Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer. BMC Bioinformatics 2011; 12:488. [PMID: 22188670 PMCID: PMC3471479 DOI: 10.1186/1471-2105-12-488] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2011] [Accepted: 12/21/2011] [Indexed: 01/09/2023] Open
Abstract
Background One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. Results Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. Conclusions Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.
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Affiliation(s)
- Stephan Gade
- German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany.
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Improved modeling of clinical data with kernel methods. Artif Intell Med 2011; 54:103-14. [PMID: 22134094 DOI: 10.1016/j.artmed.2011.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2009] [Revised: 10/22/2011] [Accepted: 11/07/2011] [Indexed: 11/20/2022]
Abstract
OBJECTIVE Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. METHODS When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. RESULTS The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. CONCLUSION For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems.
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Mankoo PK, Shen R, Schultz N, Levine DA, Sander C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS One 2011; 6:e24709. [PMID: 22073136 PMCID: PMC3207809 DOI: 10.1371/journal.pone.0024709] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2011] [Accepted: 08/17/2011] [Indexed: 12/29/2022] Open
Abstract
Background Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). Methodology/Principal Findings We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72). Conclusions/Significance We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.
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Affiliation(s)
- Parminder K. Mankoo
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Nikolaus Schultz
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Douglas A. Levine
- Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Chris Sander
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
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Yuan Y, Rueda OM, Curtis C, Markowetz F. Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer. Bioinformatics 2011; 27:2679-85. [PMID: 21804112 DOI: 10.1093/bioinformatics/btr450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts. RESULTS We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways. AVAILABILITY The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/ CONTACT yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.
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Affiliation(s)
- Yinyin Yuan
- Cambridge Research Institute, Cancer Research UK, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
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Mas VR, Mueller TF, Archer KJ, Maluf DG. Identifying biomarkers as diagnostic tools in kidney transplantation. Expert Rev Mol Diagn 2011; 11:183-96. [PMID: 21405969 DOI: 10.1586/erm.10.119] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There is a critical need for biomarkers for early diagnosis, treatment response, and surrogate end point and outcome prediction in organ transplantation, leading to a tailored and individualized treatment. Genomic and proteomic platforms have provided multiple promising new biomarkers during the last few years. However, there is still no routine application of any of these markers in clinical transplantation. This article will discuss the existing gap between biomarker discovery and clinical application in the kidney transplant setting. Approaches to implementing biomarker monitoring into clinical practice will also be discussed.
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Affiliation(s)
- Valeria R Mas
- Molecular Transplant Research Laboratory, Transplant Division, Department of Surgery, Molecular Medicine Research Building, Virginia Commonwealth University, 1220 East Broad Street, Richmond, VA 23298, USA.
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Yan Q. Toward the integration of personalized and systems medicine: challenges, opportunities and approaches. Per Med 2011; 8:1-4. [PMID: 29768783 DOI: 10.2217/pme.10.77] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Qing Yan
- PharmTao, PO Box 5672, Santa Clara, CA 95056, USA.
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Yu S, Falck T, Daemen A, Tranchevent LC, Suykens JA, De Moor B, Moreau Y. L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics 2010; 11:309. [PMID: 20529363 PMCID: PMC2906488 DOI: 10.1186/1471-2105-11-309] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2010] [Accepted: 06/08/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L(infinity), L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L(infinity) MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources. RESULTS We provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classification problems and compare its performance with the sparse L(infinity) and the averaging L1 MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. L2 MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel L2 MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing. CONCLUSIONS This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in L(infinity) MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing L2 kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL. AVAILABILITY The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html.
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Affiliation(s)
- Shi Yu
- Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium.
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Daemen A, Signoretto M, Gevaert O, Suykens JAK, De Moor B. Improved microarray-based decision support with graph encoded interactome data. PLoS One 2010; 5:e10225. [PMID: 20419106 PMCID: PMC2856685 DOI: 10.1371/journal.pone.0010225] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Accepted: 03/28/2010] [Indexed: 12/31/2022] Open
Abstract
In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.
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Affiliation(s)
- Anneleen Daemen
- Department of Electrical Engineering ESAT/SCD, Katholieke Universiteit Leuven, Leuven, Belgium.
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Yan Q. Translational bioinformatics and systems biology approaches for personalized medicine. Methods Mol Biol 2010; 662:167-178. [PMID: 20824471 DOI: 10.1007/978-1-60761-800-3_8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Systems biology and pharmacogenomics are emerging and promising fields that will provide a thorough understanding of diseases and enable personalized therapy. However, one of the most significant obstacles in the practice of personalized medicine is the translation of scientific discoveries into better therapeutic outcomes. Translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. This goal can be achieved through providing integrative methods to enable predictive models for therapeutic responses. As a media between bench and bedside, translational bioinformatics has the mission to meet challenges in the development of personalized medicine. On the biomedical side, translational bioinformatics would enable the identification of biomarkers based on systemic analyses. It can improve the understanding of the correlations between genotypes and phenotypes. It would enable novel insights of interactions and interrelationships among different parts in a whole system. On the informatics side, methods based on data integration, data mining, and knowledge representation can provide decision support for both researchers and clinicians. Data integration is not only for better data access, but also for knowledge discovery. Decision support based on translational bioinformatics means better information and workflow management, efficient literature and resource retrieval, and communication improvement. These approaches are crucial for understanding diseases and applying personalized therapeutics at systems levels.
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Milanesi L, Romano P, Castellani G, Remondini D, Liò P. Trends in modeling Biomedical Complex Systems. BMC Bioinformatics 2009; 10 Suppl 12:I1. [PMID: 19828068 PMCID: PMC2762057 DOI: 10.1186/1471-2105-10-s12-i1] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.
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Affiliation(s)
- Luciano Milanesi
- Institute of Biomedical Technology, National Research Council, Milan, Italy
| | - Paolo Romano
- Bioinformatics, National Cancer Research Institute, Genoa, Italy
| | - Gastone Castellani
- Physics Department of Bologna University, Galvani Center for Biocomplexity and INFN Italy
| | - Daniel Remondini
- Physics Department of Bologna University, Galvani Center for Biocomplexity and INFN Italy
| | - Petro Liò
- Computer Laboratory, University of Cambridge, Cambridge, UK
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