Advances and Trends in Omics Technology Development

Dai, Xiaofeng; Shen, Li

doi:10.3389/fmed.2022.911861

Cited by 195 publications

(94 citation statements)

References 290 publications

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“…And nowadays sequencing and MS are basic experimental tools in investigating the omics of a given biological system. Since a substantial amount of information is obtainable from sequencing-based methods, MS-based techniques can be used to investigate proteome, metabolome, and interactomes that do not involve DNA/RNA [44].…”

Section: Ms-based Quantitative Strategies and Analysis Of Proteome Ge...mentioning

confidence: 99%

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Šoša¹

2023

Preprint

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The literature was scoped to help place mass spectrometric (MS) platforms in the spotlight of the life sciences, as a tool of OMICS and OMICS 2.0 technologies. Moreover, today’s appliances are so handy, that they literally place a whole lab on a single chip. Methods of OMICS (and the most recently developed term OMICS 2.0) are tackled, having in mind their aptness for educational purposes, as well as their feasibility. This same topic was highlighted in many reviews that have been periodically published. The author’s initial interest was the forensic application of MS, but since these interests changed, terms such as “biosensors” were also examined, so the review included means of discovery of efficient biomarkers and clinical therapeutic targets. Aside from simply reviewing the research status and the future perspectives, this article tackles terms such as Society 5.0 and OMICS 2.0. However, within the framework of this review, physical chemistry, bioinformatics, or specific software solutions were not specifically studied. Since the topic of generating permanent collections of digital content for retrospective analysis was also reviewed, the need for loads of data – cloud storage was discussed in the context of MS. These large quantities of data are easier to operate with if generated by MS, rather than by any other commercially available assay. In this manner, the review has strengthened an in-depth understanding of the applications of MS.

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Section: Ms-based Quantitative Strategies and Analysis Of Proteome Ge...mentioning

confidence: 99%

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Šoša¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…And nowadays sequencing and MS are basic experimental tools in investigating the omics of a given biological system. Since a substantial amount of information is obtainable from sequencingbased methods, MS-based techniques can be used to investigate proteome, metabolome, and interactomes that do not involve DNA/RNA [43].…”

Section: Ms-based Quantitative Strategies and Analysis Of Proteome Ge...mentioning

confidence: 99%

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Šoša¹

2022

Preprint

View full text Add to dashboard Cite

The literature was scoped to help place mass spectrometric (MS) platforms in the spotlight of the life sciences, as a tool of OMICS and OMICS 2.0 technologies. These methods were discussed, and their feasibility was highlighted in many reviews that have been periodically published, in addition to the most recently developed term OMICS 2.0. This author’s primary interest was the forensic application of MS, but since these interests got broadened, terms such as “biosensors” were also included. So, means of discovery of efficient biomarkers and clinical therapeutic targets were also reviewed. However, within the framework of this review, physical chemistry, bioinformatics, or specific software solutions were not specifically scoped. Aside from simply reviewing the research status and the future perspectives, this article tackles terms such as Society 5.0 and OMICS 2.0. Since the topic of generating permanent collections of digital content for retrospective analysis was reviewed, what requires loads of data – cloud storage is discussed in the context of MS. In this manner, the review has strengthened an in-depth understanding of the applications of MS. These large quantities of data are easier to operate with if generated by MS, rather than by any other commercially available assay.

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“…With the advent of high-throughput technologies, multiple omics data are more and more available to characterize the molecular portraits of patients, notably in oncology [1]. Analyzing this mix of data sources and leveraging their information to improve our understanding of the disease's underlying biological phenomena remain challenging.…”

Section: Introductionmentioning

confidence: 99%

CustOmics: A versatile deep-learning based strategy for multi-omics integration

Hakim

Pradat²,

Michiels³

et al. 2023

PLoS Comput Biol

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The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease’s underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source’s singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley Additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

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Advances and Trends in Omics Technology Development

Cited by 195 publications

References 290 publications

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Mass Spectrometry-Based Techniques Towards OMICS 2.0

CustOmics: A versatile deep-learning based strategy for multi-omics integration

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