On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification

Escobar, Matheus S.; Kaneko, Hiromasa; Funatsu, Kimito

doi:10.1016/j.compchemeng.2016.12.009

Cited by 17 publications

(15 citation statements)

References 34 publications

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“…Outliers and abnormal data have an adverse effect on the predictive ability of soft sensors, and should therefore be preprocessed (i.e., deleted) [38]. We believe that the use of our proposed method and appropriate DBM will improve the predictive accuracy of adaptive soft sensor models, allowing chemical plants to be operated more effectively and stably.…”

Section: Discussionmentioning

confidence: 99%

Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors

Kaneko

Funatsu

2015

Chemometrics and Intelligent Laboratory Systems

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a b s t r a c tSoft sensors are an essential tool for controlling chemical and industrial plants. To adapt to new process states, these soft sensors require adaptation mechanisms. As the performance of adaptive soft sensors depends on the quality of its database and the size of which will have some upper limit, a database monitoring index (DMI) has been developed. Additional information is only added to the database if it has a sufficiently high DMI value. In this study, we propose a DMI optimization method and adaptive database management scheme. DMI hyperparameters are set automatically using an initial database, and the main database adapts to new process states by storing data with large prediction errors. Case studies using simulated and real industrial datasets confirm that the proposed scheme enables soft sensors to operate with high predictive accuracy over the long term.

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Section: Discussionmentioning

confidence: 99%

Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors

Kaneko

Funatsu

2015

Chemometrics and Intelligent Laboratory Systems

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“…In this case, the new considered space is called latent space. Generative Topographic Mapping (GTM) [42] can be seen as a graphical DRM. A 2D manifold is fitted to the feature space points by means of mathematical functions.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Inverse‐QSPR for de novo Design: A Review

Gantzer

Creton

Nieto‐Draghi

2019

Molecular Informatics

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The use of computer tools to solve chemistry‐related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features – descriptors – are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules – de novo design – is currently a high‐interest subject. Inverse‐QSPR (i‐QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i‐QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each.

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“…A GTM [35] is the mathematically founded counterpart of a self-organizing map. We used the GTM implementation from Escobar et al [37,38] Full Paper www.molinf.com Table 2. Points from a d-dimensional space (referred to as the data space, which is equivalent to disease signatures in our case) are projected onto a 2D map (called the latent space).…”

Section: Generative Topographic Mapping To Visualize Disease-disease mentioning

confidence: 99%

“…Figure 4 shows a generative topographic map (GTM) [35,36,37,38] for the 79 diseases produced from the pathway-based glycogene signatures. Collectively, these results suggest that commonalities between different diseases can be identified using pathway-based glycogene signatures.…”

Section: Identification Of Commonalities Among Different Diseasesmentioning

confidence: 99%

Omics‐based Identification of Glycan Structures as Biomarkers for a Variety of Diseases

Akiyoshi

Iwata

Berenger

et al. 2019

Molecular Informatics

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Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of glycogenes (glycosyltransferases and glycosidases) for 79 different diseases. A biological pathway-based glycogene signature was constructed to identify the alteration in glycan biosynthesis and the associated glycan structures for each disease state. The degradation of N-glycan and keratan sulfate, for example, may promote the growth or metastasis of multiple types of cancer, including endometrial, gastric, and nasopharyngeal. Our results also revealed that commonalities between diseases can be interpreted using glycogene expression patterns, as well as the associated glycan structure patterns at the level of the affected pathway. The proposed method is expected to be useful for understanding the relationships between glycans, glycogenes, and disease and identifying disease-specific glycan biomarkers.

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On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification

Cited by 17 publications

References 34 publications

Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors

Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors

Inverse‐QSPR for de novo Design: A Review

Omics‐based Identification of Glycan Structures as Biomarkers for a Variety of Diseases

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