scholarly journals Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Nanomaterials ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2385
Author(s):  
Tae Hwan Shin ◽  
Saraswathy Nithiyanandam ◽  
Da Yeon Lee ◽  
Do Hyeon Kwon ◽  
Ji Su Hwang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biomedical Applications ◽  
Traction Force ◽  
Molecular Profiling ◽  
Omics Data ◽  
Network Integration ◽  
Rigidity Sensing ◽  
Integrated Omics ◽  
Biophysical Changes ◽  
Integration Analysis

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Using machine learning approaches for multi-omics data analysis: A review

2021 ◽  
Vol 49 ◽  
pp. 107739
Author(s):  
Parminder S. Reel ◽  
Smarti Reel ◽  
Ewan Pearson ◽  
Emanuele Trucco ◽  
Emily Jefferson
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Omics Data ◽  
Learning Approaches ◽  
Omics Data Analysis

scholarly journals A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 565
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Shota Tanaka ◽  
Shunsaku Takayanagi ◽  
Hirokazu Takami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Situation ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Omics Data ◽  
Amount Of Information ◽  
Oncology Research ◽  
Omics Analysis ◽  
Learning Techniques ◽  
Comprehensive Survey

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Techniques for Multi-Omics Data Incorporating Machine Learning and System Genomics

2021 ◽  
Author(s):  
Md. Nasfikur Rahman Khan ◽  
Sarmila Yesmin ◽  
Mahbuba Aktar ◽  
Kuraish Bin Quader Chowdhury ◽  
Kashshaf Labeeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Omics Data

scholarly journals Computational prediction of inter-species relationships through omics data analysis and machine learning

2018 ◽  
Vol 19 (S14) ◽  
Author(s):  
Diogo Manuel Carvalho Leite ◽  
Xavier Brochet ◽  
Grégory Resch ◽  
Yok-Ai Que ◽  
Aitana Neves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Computational Prediction ◽  
Omics Data ◽  
Species Relationships ◽  
Omics Data Analysis

scholarly journals A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

BMC Genomics ◽  
2015 ◽  
Vol 16 (Suppl 1) ◽  
pp. S2 ◽  
Author(s):  
Anna L Swan ◽  
Dov J Stekel ◽  
Charlie Hodgman ◽  
David Allaway ◽  
Mohammed H Alqahtani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Omics Data ◽  
Biologically Relevant

scholarly journals Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Hae Deok Jung ◽  
Yoo Jin Sung ◽  
Hyun Uk Kim
Keyword(s):  
Machine Learning ◽  
Computational Modeling ◽  
Cancer Treatment ◽  
Effective Treatment ◽  
Cancer Cells ◽  
Computational Models ◽  
Omics Data ◽  
Drug Resistant ◽  
Learning Models ◽  
Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

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 Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252096
Author(s):  
Maria B. Rabaglino ◽  
Alan O’Doherty ◽  
Jan Bojsen-Møller Secher ◽  
Patrick Lonergan ◽  
Poul Hyttel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Focal Adhesion ◽  
Learning Approach ◽  
Omics Data ◽  
Machine Learning Approach ◽  
Cluster 2 ◽  
Omics Data Integration

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

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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