Machine Learning for In Silico Modeling of Tumor Growth

Jeanquartier, Fleur; Jean-Quartier, Claire; Kotlyar, Max; Tokár, Tomáš; Hauschild, Anne-Christin; Jurišica, Igor; Holzinger, Andreas

doi:10.1007/978-3-319-50478-0_21

Cited by 13 publications

(13 citation statements)

References 104 publications

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“…Artificial intelligence in cancer science includes not only classification but also diagnostics [30] as well as prediction of clinical features or identification of interactions. Most importantly, it includes research integrating multi-omics data type [31]. A further example, such as in [32], could be likewise included, as well as the one in [33].…”

Section: Introductionmentioning

confidence: 99%

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Lin

Zhang

Cao

et al. 2020

Genes

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With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

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Section: Introductionmentioning

confidence: 99%

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Lin

Zhang

Cao

et al. 2020

Genes

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“…In addition, our future research will focus on two areas in order to improve the quality and utility of the PPI database. First, we will improve the performance of PPIExtractor with the introduction of the popular deep learning method [38]. Second, we plan to extract the PPIs associated with human malignant neoplasms from full texts of the article instead of abstracts only which is recently made feasible with PMC Open Access BioC RESTful server (https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/).…”

Section: Discussionmentioning

confidence: 99%

HMNPPID—human malignant neoplasm protein–protein interaction database

Yang

Zhao

et al. 2019

Hum Genomics

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BackgroundProtein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available.ResultsIn this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm.ConclusionsHMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

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“…Classification of disease subtypes, subjects, brain regions, and gradings are often based on ML approaches via automatically segmenting brain MRI data [24]. Making use of such databases, ML not only helps in (semi-)automatically segmenting images, but it is also a tool for trying to answer several research questions, for example predicting tumor growth [28] or investigating minimal tumor burden and therapy resistance by cancer patients [48,49]. Some case reports also show AI outperforming human domain experts [32,19,55].…”

Section: Databases For Ai/mlmentioning

confidence: 99%

Developments in AI and Machine Learning for Neuroimaging

O’Sullivan

Jeanquartier

Jean-Quartier

et al. 2020

Lecture Notes in Computer Science

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This paper reviews guidelines on how medical imaging analysis can be enhanced by Artificial Intelligence (AI) and Machine Learning (ML). In addition to outlining current and potential future developments, we also provide background information on chemical imaging and discuss the advantages of Explainable AI. We hypothesize that it is a matter of AI to find an invariably recurring parameter that has escaped human attention (e.g. due to noisy data). There is great potential in AI to illuminate the feature space of successful models.

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Machine Learning for In Silico Modeling of Tumor Growth

Cited by 13 publications

References 104 publications

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

HMNPPID—human malignant neoplasm protein–protein interaction database

Developments in AI and Machine Learning for Neuroimaging

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