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Rintala TJ, Fortino V. COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms. PLoS Comput Biol 2024; 20:e1012275. [PMID: 39102448 PMCID: PMC11326705 DOI: 10.1371/journal.pcbi.1012275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 08/15/2024] [Accepted: 06/25/2024] [Indexed: 08/07/2024] Open
Abstract
Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies.
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Affiliation(s)
- Teemu J. Rintala
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
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2
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Hajim WI, Zainudin S, Mohd Daud K, Alheeti K. Optimized models and deep learning methods for drug response prediction in cancer treatments: a review. PeerJ Comput Sci 2024; 10:e1903. [PMID: 38660174 PMCID: PMC11042005 DOI: 10.7717/peerj-cs.1903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
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Affiliation(s)
- Wesam Ibrahim Hajim
- Department of Applied Geology, College of Sciences, Tirkit University, Tikrit, Salah ad Din, Iraq
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Khattab Alheeti
- Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq
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3
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Tschodu D, Lippoldt J, Gottheil P, Wegscheider AS, Käs JA, Niendorf A. Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer. Sci Rep 2023; 13:16402. [PMID: 37798300 PMCID: PMC10556090 DOI: 10.1038/s41598-023-41090-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/22/2023] [Indexed: 10/07/2023] Open
Abstract
Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.
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Affiliation(s)
- Dimitrij Tschodu
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
| | - Jürgen Lippoldt
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
| | - Pablo Gottheil
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
| | - Anne-Sophie Wegscheider
- Institute for Histology, Cytology and Molecular Diagnostics, MVZ Prof. Dr. med. A. Niendorf Pathologie Hamburg-West GmbH, 22767, Hamburg, Germany
| | - Josef A Käs
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
| | - Axel Niendorf
- Institute for Histology, Cytology and Molecular Diagnostics, MVZ Prof. Dr. med. A. Niendorf Pathologie Hamburg-West GmbH, 22767, Hamburg, Germany.
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4
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An element-wise kernel learning framework. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04020-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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5
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Hwangbo S, Lee S, Lee S, Hwang H, Kim I, Park T. Kernel-based hierarchical structural component models for pathway analysis. Bioinformatics 2022; 38:3078-3086. [PMID: 35460238 DOI: 10.1093/bioinformatics/btac276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 04/08/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Suhyun Hwangbo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.,Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Sungyoung Lee
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Sejong, 05006, Korea
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, QC, H3A 1B1, Canada
| | - Inyoung Kim
- Department of Statistics, Virginia Tech, Blacksburg, Virginia, 24060, U.S.A
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Korea.,Department of Statistics, Seoul National University, Seoul, 151-747, Korea
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6
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Correa R, Alonso-Pupo N, Hernández Rodríguez EW. Multi-omics data integration approaches for precision oncology. Mol Omics 2022; 18:469-479. [DOI: 10.1039/d1mo00411e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...
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7
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Qu J, Cui Y. Gene set analysis with graph-embedded kernel association test. Bioinformatics 2021; 38:1560-1567. [PMID: 34935928 PMCID: PMC8896609 DOI: 10.1093/bioinformatics/btab851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/20/2021] [Accepted: 12/16/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Kernel-based association test (KAT) has been a popular approach to evaluate the association of expressions of a gene set (e.g. pathway) with a phenotypic trait. KATs rely on kernel functions which capture the sample similarity across multiple features, to capture potential linear or non-linear relationship among features in a gene set. When calculating the kernel functions, no network graphical information about the features is considered. While genes in a functional group (e.g. a pathway) are not independent in general due to regulatory interactions, incorporating regulatory network (or graph) information can potentially increase the power of KAT. In this work, we propose a graph-embedded kernel association test, termed gKAT. gKAT incorporates prior pathway knowledge when constructing a kernel function into hypothesis testing. RESULTS We apply a diffusion kernel to capture any graph structures in a gene set, then incorporate such information to build a kernel function for further association test. We illustrate the geometric meaning of the approach. Through extensive simulation studies, we show that the proposed gKAT algorithm can improve testing power compared to the one without considering graph structures. Application to a real dataset further demonstrate the utility of the method. AVAILABILITY AND IMPLEMENTATION The R code used for the analysis can be accessed at https://github.com/JialinQu/gKAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jialin Qu
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
| | - Yuehua Cui
- To whom correspondence should be addressed.
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8
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Mourragui SMC, Loog M, Vis DJ, Moore K, Manjon AG, van de Wiel MA, Reinders MJT, Wessels LFA. Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proc Natl Acad Sci U S A 2021; 118:e2106682118. [PMID: 34873056 PMCID: PMC8670522 DOI: 10.1073/pnas.2106682118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.
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Affiliation(s)
- Soufiane M C Mourragui
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Marco Loog
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Daniel J Vis
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Kat Moore
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Anna G Manjon
- Division of Cell Biology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Mark A van de Wiel
- Epidemiology and Biostatistics, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge CB2 0SR, United Kingdom
| | - Marcel J T Reinders
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands;
- Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
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9
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Carpenter CM, Zhang W, Gillenwater L, Severn C, Ghosh T, Bowler R, Kechris K, Ghosh D. PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes. PLoS Comput Biol 2021; 17:e1008986. [PMID: 34679079 PMCID: PMC8565741 DOI: 10.1371/journal.pcbi.1008986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/03/2021] [Accepted: 10/13/2021] [Indexed: 02/02/2023] Open
Abstract
High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.
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Affiliation(s)
- Charlie M. Carpenter
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical campus, Denver, Colorado, United States of America
| | - Weiming Zhang
- Syneos Health, Morrisville, North Carolina, United States of America
| | - Lucas Gillenwater
- Computational Bioscience Program, University of Colorado Denver, Anschutz medical campus, Denver, Colorado, United States of America
| | - Cameron Severn
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical campus, Denver, Colorado, United States of America
| | - Tusharkanti Ghosh
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical campus, Denver, Colorado, United States of America
| | - Russell Bowler
- Department of Medicine, National Jewish Health, Denver; University of Colorado Denver, Anschutz Medical Campus, Denver, Colorado, United States of America
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical campus, Denver, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical campus, Denver, Colorado, United States of America
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10
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Abstract
This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.
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11
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Manica M, Bunne C, Mathis R, Cadow J, Ahsen ME, Stolovitzky GA, Martínez MR. COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics 2021; 37:2070-2072. [PMID: 33241320 PMCID: PMC8337002 DOI: 10.1093/bioinformatics/btaa942] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/25/2020] [Accepted: 10/26/2020] [Indexed: 11/23/2022] Open
Abstract
Summary The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. Availability and implementation COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matteo Manica
- Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.,Institute of Molecular Systems Biology, ETH Zürich, Zürich, ZH 8093, Switzerland
| | - Charlotte Bunne
- Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.,Institute for Machine Learning, ETH Zürich, Zürich, ZH 8092, Switzerland
| | - Roland Mathis
- Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland
| | - Joris Cadow
- Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland
| | - Mehmet Eren Ahsen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA
| | - Gustavo A Stolovitzky
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA.,Translational Systems Biology and Nanobiotechnology, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - María Rodríguez Martínez
- Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland
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12
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Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021; 19:3735-3746. [PMID: 34285775 PMCID: PMC8258788 DOI: 10.1016/j.csbj.2021.06.030] [Citation(s) in RCA: 166] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/25/2022] Open
Abstract
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Corresponding author.
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13
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Wilson CM, Li K, Sun Q, Kuan PF, Wang X. Fenchel duality of Cox partial likelihood with an application in survival kernel learning. Artif Intell Med 2021; 116:102077. [PMID: 34020756 PMCID: PMC8159024 DOI: 10.1016/j.artmed.2021.102077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 04/14/2021] [Accepted: 04/19/2021] [Indexed: 11/30/2022]
Abstract
The Cox proportional hazard model is one of the most widely used methods in modeling time-to-event data in the health sciences. Due to the simplicity of the Cox partial likelihood function, many machine learning algorithms use it for survival data. However, due to the nature of censored data, the optimization problem becomes intractable when more complicated regularization is employed, which is necessary when dealing with high dimensional omic data. In this paper, we show that a convex conjugate function of the Cox loss function based on Fenchel duality exists, and provide an alternative framework to optimization based on the primal form. Furthermore, the dual form suggests an efficient algorithm for solving the kernel learning problem with censored survival outcomes. We illustrate performance and properties of the derived duality form of Cox partial likelihood loss in multiple kernel learning problems with simulated and the Skin Cutaneous Melanoma TCGA datasets.
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Affiliation(s)
- Christopher M Wilson
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Kaiqiao Li
- Department of Applied Math & Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Qiang Sun
- Department of Statistical Sciences, University of Toronto, Ontario M5S 3G3, Canada
| | - Pei Fen Kuan
- Department of Applied Math & Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Xuefeng Wang
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
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14
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Uzunangelov V, Wong CK, Stuart JM. Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge. PLoS Comput Biol 2021; 17:e1008878. [PMID: 33861732 PMCID: PMC8081343 DOI: 10.1371/journal.pcbi.1008878] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/28/2021] [Accepted: 03/15/2021] [Indexed: 02/03/2023] Open
Abstract
Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE has comparable or improved performance relative to state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality.
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Affiliation(s)
- Vladislav Uzunangelov
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
| | - Christopher K. Wong
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
| | - Joshua M. Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America
- * E-mail:
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15
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Chebanov DK, Mikhaylova IN. On the Methods of Artificial Intelligence for Analysis of Oncological Data. AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2020. [DOI: 10.3103/s0005105520050027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Crawford J, Greene CS. Incorporating biological structure into machine learning models in biomedicine. Curr Opin Biotechnol 2020; 63:126-134. [PMID: 31962244 PMCID: PMC7308204 DOI: 10.1016/j.copbio.2019.12.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/17/2019] [Accepted: 12/19/2019] [Indexed: 12/19/2022]
Abstract
In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is crucial, incorporating prior knowledge in the form of structured data can be particularly useful. The area of research would benefit from performant open source implementations and independent benchmarking efforts.
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Affiliation(s)
- Jake Crawford
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA, United States.
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Zeng L, Yu Z, Zhao H. A Pathway-Based Kernel Boosting Method for Sample Classification Using Genomic Data. Genes (Basel) 2019; 10:E670. [PMID: 31480483 PMCID: PMC6770716 DOI: 10.3390/genes10090670] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 08/25/2019] [Accepted: 08/28/2019] [Indexed: 11/16/2022] Open
Abstract
The analysis of cancer genomic data has long suffered "the curse of dimensionality." Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify informative pathways but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classification loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites and metastasis status. Our results show that PKB outperforms other methods and identifies pathways relevant to the outcome variables.
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Affiliation(s)
- Li Zeng
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Zhaolong Yu
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA.
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.
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