Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Takahashi, Satoshi; Asada, Ken; Takasawa, Ken; Shimoyama, Ryo; Sakai, Akira; Bolatkan, Amina; Shinkai, Norio; Kobayashi, Kazuma; Komatsu, Masaaki; Kaneko, Syuzo; Sese, Jun; Hamamoto, Ryuji

doi:10.3390/biom10101460

Cited by 56 publications

(35 citation statements)

References 44 publications

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“…Bioinformatics analysis showed that the expression levels of six of the 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with survival in LUAD patients, and pathway analysis indicated that major cancer signaling pathways were altered in the subtypes. Expanding on this method, we identified survival-related subtypes of non-small cell lung cancer from six categories of TCGA multi-omics datasets (miRNA, mRNA, DNA methylation, somatic mutation, copy number variation, and reverse phase protein array) [106]. As a result, the subtype named the Integrated Survival Subtype, which combined the six types of data, successfully separated the poor and good prognosis groups of lung cancer patients with a statistically significant difference.…”

Section: Application Of Machine Learning and Deep Learning Techniquesmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

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144

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In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

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Section: Application Of Machine Learning and Deep Learning Techniquesmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

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144

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“…In addition, there is a growing need for multimodal analysis, such as integrated analysis of genomic and epigenomic data, not just data from one modality. This kind of advanced analysis using a large amount of data is difficult to perform using conventional statistical methods, but nowadays, by proactively introducing artificial intelligence (AI) with machine learning (ML) and deep learning (DL) technologies at its core, good results can be obtained (29)(30)(31). In our view, there are four properties of ML and DL that are of particular importance.…”

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

et al. 2021

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With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, “precision medicine,” which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

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“…One solution to this problem is the use of multi-omics analysis. Even if the appropriate data type required is unknown, the combination of one or more data types may prove useful [ 11 , 12 ]. However, there remains the problem that it is impossible for humans to find principals and make a decision because of too much information.…”

Section: Introductionmentioning

confidence: 99%

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

Takahashi

Takahashi²,

Tanaka

et al. 2021

Biomolecules

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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.

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Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Cited by 56 publications

References 44 publications

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

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

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