scholarly journals A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

2021 ◽  
Vol 22 (6) ◽  
pp. 2822
Author(s):  
Efstathios Iason Vlachavas ◽  
Jonas Bohn ◽  
Frank Ückert ◽  
Sylvia Nürnberg
Keyword(s):  
Cancer Research ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Molecular Data ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Translational Cancer Research ◽  
Using Data ◽  
Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

scholarly journals Network reconstruction for trans acting genetic loci using multi-omics data and prior information

2020 ◽  
Author(s):  
Johann S. Hawe ◽  
Ashis Saha ◽  
Melanie Waldenberger ◽  
Sonja Kunze ◽  
Simone Wahl ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Prior Information ◽  
Network Inference ◽  
Model Systems ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Cohort Data ◽  
Inference Methods

AbstractBackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information.ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

scholarly journals Working with Human Tissues for Translational Cancer Research

10.3791/53189 ◽  
2015 ◽  
Author(s):  
Alexandre Reuben ◽  
Vancheswaran Gopalakrishnan ◽  
Heidi E. Wagner ◽  
Christine N. Spencer ◽  
Jacob Austin-Breneman ◽  
...  
Keyword(s):  
Cancer Research ◽  
Human Tissues ◽  
Translational Cancer Research

scholarly journals Stem Cells in Translational Cancer Research

2015 ◽  
Vol 2015 ◽  
pp. 1-2
Author(s):  
Oswaldo Keith Okamoto ◽  
Ander Matheu ◽  
Luca Magnani
Keyword(s):  
Stem Cells ◽  
Cancer Research ◽  
Translational Cancer Research

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

2021 ◽  
pp. 561-569
Author(s):  
Steven A. Eschrich ◽  
Jamie K. Teer ◽  
Phillip Reisman ◽  
Erin Siegel ◽  
Chandan Challa ◽  
...  
Keyword(s):  
Cancer Research ◽  
Precision Medicine ◽  
Data Warehouse ◽  
Clinical Data ◽  
Molecular Data ◽  
Project Planning ◽  
Cancer Genome ◽  
Open Communication ◽  
Clinical Data Warehouse ◽  
Research Studies

PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

scholarly journals A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

2015 ◽  
Vol 75 (24) ◽  
pp. 5194-5201 ◽  
Author(s):  
Rebecca S. Jacobson ◽  
Michael J. Becich ◽  
Roni J. Bollag ◽  
Girish Chavan ◽  
Julia Corrigan ◽  
...  
Keyword(s):  
Cancer Research ◽  
Clinical Data ◽  
Translational Cancer Research

scholarly journals A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kalpana Raja ◽  
Matthew Patrick ◽  
Yilin Gao ◽  
Desmond Madu ◽  
Yuyang Yang ◽  
...  
Keyword(s):  
Text Mining ◽  
High Throughput ◽  
Literature Mining ◽  
Biological Knowledge ◽  
Omics Data ◽  
Huge Number ◽  
Automated Generation ◽  
The Past ◽  
Independent Information ◽  
Recent Advancement

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

scholarly journals Machine learning for single cell genomics data analysis

2021 ◽  
Author(s):  
Félix Raimundo ◽  
Laetitia Papaxanthos ◽  
Céline Vallot ◽  
Jean-Philippe Vert
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Network Inference ◽  
Method Development ◽  
Biological Knowledge ◽  
Omics Data ◽  
Gene Regulatory Network Inference ◽  
Multimodal Data ◽  
Low Dimensional ◽  
Type Classification

AbstractSingle-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data, machine learning (ML)-based model are increasingly used due to their flexibility, scalability, and impressive success in other fields. In recent years, we have seen a surge of new ML-based method development for low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference or multimodal data integration. To help readers navigate this fast-moving literature, we survey in this review recent advances in ML approaches developed to analyze single-cell omics data, focusing mainly on peer-reviewed publications published in the last two years (2019-2020).

scholarly journals Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053674
Author(s):  
Enrico Glaab ◽  
Armin Rauschenberger ◽  
Rita Banzi ◽  
Chiara Gerardi ◽  
Paula Garcia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Biomarker Discovery ◽  
Biomedical Literature ◽  
Molecular Signature ◽  
Biological Knowledge ◽  
Measurement Technology ◽  
Omics Data ◽  
Patient Stratification ◽  
Complex Disorders

ObjectiveTo review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.DesignScoping review.MethodsWe searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.ResultsOverall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.ConclusionsWhile most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

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