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Ken Takasawa

Bio: Ken Takasawa is an academic researcher from University of Miyazaki. The author has contributed to research in topics: DNA methylation & Induced pluripotent stem cell. The author has an hindex of 8, co-authored 15 publications receiving 139 citations.

Papers
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Journal ArticleDOI
26 Nov 2020-Cancers
TL;DR: The history of AI technology as well as the state of the art of medical AI are introduced, focusing on the field of oncology, where AI is expected to play an important role in realizing the current global trend of precision medicine.
Abstract: 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.

82 citations

Journal ArticleDOI
30 Dec 2019
TL;DR: The importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine is discussed and the current progress of artificial intelligence technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data.
Abstract: To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

55 citations

Journal ArticleDOI
19 Oct 2020
TL;DR: A novel method to accurately predict the survival of patients with lung cancer using multi-omics data and a machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately Predict the integration survival subtypes.
Abstract: Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

33 citations

Journal ArticleDOI
TL;DR: This study identified a differentially methylated region (DMR) at a distal region in the TERT promoter between human iPSCs and their parental somatic cells and suggested thatThe TERT transcription was enhanced by DNA methylation at the Tert-DMR via binding to nuclear lamina during reprogramming.
Abstract: During reprogramming into human induced pluripotent stem cells (iPSCs), several stem cell marker genes are induced, such as OCT-4, NANOG, SALL4, and TERT. OCT-4, NANOG, and SALL4 gene expression can be regulated by DNA methylation. Their promoters become hypomethylated in iPSCs during reprogramming, leading to their induced expression. However, epigenetic regulation of the TERT gene remains unclear. In this study, we focused on epigenetic regulation of the human TERT gene and identified a differentially methylated region (DMR) at a distal region in the TERT promoter between human iPSCs and their parental somatic cells. Interestingly, the TERT-DMR was highly methylated in iPSCs, but low-level methylation was observed in their parental somatic cells. Region-specific, methylated-promoter assays showed that the methylated TERT-DMR up-regulated the promoter activity in iPSCs. In addition, Lamin B1 accumulated at the TERT-DMR in iPSCs, but not in their parent somatic cells. These results suggested that the TERT transcription was enhanced by DNA methylation at the TERT-DMR via binding to nuclear lamina during reprogramming. Our findings shed light on a new functional aspect of DNA methylation in gene expression.

31 citations

Journal ArticleDOI
30 Mar 2020
TL;DR: This work combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas focusing on lung adenocarcinoma (LUAD) and was able to successfully subclass patients according to survival.
Abstract: Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

30 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors explore different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease.

164 citations

Journal ArticleDOI
29 Jul 2020
TL;DR: The classification accuracy of FRCNN was better than that of the dermatologists, and it is planned to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.
Abstract: Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

116 citations

Journal ArticleDOI
30 Mar 2021-Cancers
TL;DR: A novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deepLearning model on the small amount of labeled medical images is proposed.
Abstract: Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

109 citations

Journal ArticleDOI
TL;DR: This work presents current evidence documenting promoter hypermethylation and high levels of gene expression, offers insights into the possible mechanisms by which this occurs, and discusses the potential implications for both research and clinical applications.
Abstract: DNA methylation is a stable epigenetic modification that contributes to the spatiotemporal regulation of gene expression. The manner in which DNA methylation contributes to transcriptional control is dependent on the biological context, including physiological state and the properties of the DNA itself. Classically, dense promoter DNA methylation is associated with transcriptional repression. However, growing evidence suggests that this association may not always hold true, and promoter hypermethylation now also appears to be associated with high transcriptional activity. Furthermore, in a selection of contexts, increasing levels of promoter methylation correlate directly with increased gene expression. These findings postulate a context-dependent model whereby epigenetic contributions to transcriptional regulation occur in a more complex and dynamic manner. We present current evidence documenting promoter hypermethylation and high levels of gene expression, offer insights into the possible mechanisms by which this occurs, and discuss the potential implications for both research and clinical applications.

102 citations