Biomolecules

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4 pages, 2673 KiB

Open AccessEditorial

Application of Artificial Intelligence for Medical Research

by Ryuji Hamamoto

Biomolecules 2021, 11(1), 90; https://doi.org/10.3390/biom11010090 - 12 Jan 2021

Cited by 19 | Viewed by 5122

Abstract

The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [...] Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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Research

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13 pages, 2118 KiB

Open AccessArticle

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

by Norio Yamamoto, Shintaro Sukegawa, Akira Kitamura, Ryosuke Goto, Tomoyuki Noda, Keisuke Nakano, Kiyofumi Takabatake, Hotaka Kawai, Hitoshi Nagatsuka, Keisuke Kawasaki, Yoshihiko Furuki and Toshifumi Ozaki

Biomolecules 2020, 10(11), 1534; https://doi.org/10.3390/biom10111534 - 10 Nov 2020

Cited by 66 | Viewed by 14704

Abstract

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal [...] Read more.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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17 pages, 2491 KiB

Open AccessEditor’s ChoiceArticle

Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information

by Ai Dozen, Masaaki Komatsu, Akira Sakai, Reina Komatsu, Kanto Shozu, Hidenori Machino, Suguru Yasutomi, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Ryu Matsuoka, Daisuke Aoki, Akihiko Sekizawa and Ryuji Hamamoto

Biomolecules 2020, 10(11), 1526; https://doi.org/10.3390/biom10111526 - 08 Nov 2020

Cited by 49 | Viewed by 6784

Abstract

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we [...] Read more.

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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16 pages, 3013 KiB

Open AccessArticle

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

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

Biomolecules 2020, 10(10), 1460; https://doi.org/10.3390/biom10101460 - 19 Oct 2020

Cited by 36 | Viewed by 4939

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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16 pages, 3028 KiB

Open AccessArticle

Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations

by Kazuma Kobayashi, Amina Bolatkan, Shuichiro Shiina and Ryuji Hamamoto

Biomolecules 2020, 10(9), 1249; https://doi.org/10.3390/biom10091249 - 28 Aug 2020

Cited by 18 | Viewed by 2828

Abstract

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable’s [...] Read more.

Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second, each input variable’s contribution to the prediction is usually difficult to interpret, owing to multiple nonlinear operations. Third, genetic data features sometimes have no innate structure. To alleviate these problems, we propose a modification to Diet Networks by adding element-wise input scaling. The original Diet Networks concept can considerably reduce the number of parameters of the fully-connected layers by taking the transposed data matrix as an input to its auxiliary network. The efficacy of the proposed architecture was evaluated on a binary classification task for lung cancer histology, that is, adenocarcinoma or squamous cell carcinoma, from a somatic mutation profile. The dataset consisted of 950 cases, and 5-fold cross-validation was performed for evaluating the model performance. The model achieved a prediction accuracy of around 80% and showed that our modification markedly stabilized the learning process. Also, latent representations acquired inside the model allowed us to interpret the relationship between somatic mutation sites for the prediction. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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11 pages, 1057 KiB

Open AccessArticle

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder

by Dongmei Ai, Yuduo Wang, Xiaoxin Li and Hongfei Pan

Biomolecules 2020, 10(9), 1207; https://doi.org/10.3390/biom10091207 - 20 Aug 2020

Cited by 27 | Viewed by 4024

Abstract

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. [...] Read more.

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE). Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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13 pages, 1305 KiB

Open AccessArticle

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

by Shunichi Jinnai, Naoya Yamazaki, Yuichiro Hirano, Yohei Sugawara, Yuichiro Ohe and Ryuji Hamamoto

Biomolecules 2020, 10(8), 1123; https://doi.org/10.3390/biom10081123 - 29 Jul 2020

Cited by 131 | Viewed by 8943

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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17 pages, 3619 KiB

Open AccessArticle

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

by Elke Korb, Murat Bağcıoğlu, Erika Garner-Spitzer, Ursula Wiedermann, Monika Ehling-Schulz and Irma Schabussova

Biomolecules 2020, 10(7), 1058; https://doi.org/10.3390/biom10071058 - 16 Jul 2020

Cited by 11 | Viewed by 3790

Abstract

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) [...] Read more.

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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10 pages, 4215 KiB

Open AccessArticle

Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering

by Naoki Yamato, Mana Matsuya, Hirohiko Niioka, Jun Miyake and Mamoru Hashimoto

Biomolecules 2020, 10(7), 1012; https://doi.org/10.3390/biom10071012 - 08 Jul 2020

Cited by 7 | Viewed by 2829

Abstract

Semantic segmentation with deep learning to extract nerves from label-free endoscopic images obtained using coherent anti-Stokes Raman scattering (CARS) for nerve-sparing surgery is described. We developed a CARS rigid endoscope in order to identify the exact location of peripheral nerves in surgery. Myelinated [...] Read more.

Semantic segmentation with deep learning to extract nerves from label-free endoscopic images obtained using coherent anti-Stokes Raman scattering (CARS) for nerve-sparing surgery is described. We developed a CARS rigid endoscope in order to identify the exact location of peripheral nerves in surgery. Myelinated nerves are visualized with a CARS lipid signal in a label-free manner. Because the lipid distribution includes other tissues as well as nerves, nerve segmentation is required to achieve nerve-sparing surgery. We propose using U-Net with a VGG16 encoder as a deep learning model and pre-training with fluorescence images, which visualize the lipid distribution similar to CARS images, before fine-tuning with a small dataset of CARS endoscopy images. For nerve segmentation, we used 24 CARS and 1,818 fluorescence nerve images of three rabbit prostates. We achieved label-free nerve segmentation with a mean accuracy of 0.962 and an

F_{1}

value of 0.860. Pre-training on fluorescence images significantly improved the performance of nerve segmentation in terms of the mean accuracy and

F_{1}

value (

p < 0.05

). Nerve segmentation of label-free endoscopic images will allow for safer endoscopic surgery, while reducing dysfunction and improving prognosis after surgery. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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13 pages, 2142 KiB

Open AccessArticle

Deep Neural Networks for Dental Implant System Classification

by Shintaro Sukegawa, Kazumasa Yoshii, Takeshi Hara, Katsusuke Yamashita, Keisuke Nakano, Norio Yamamoto, Hitoshi Nagatsuka and Yoshihiko Furuki

Biomolecules 2020, 10(7), 984; https://doi.org/10.3390/biom10070984 - 01 Jul 2020

Cited by 98 | Viewed by 9171

Abstract

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic [...] Read more.

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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13 pages, 2184 KiB

Open AccessArticle

Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks

by Saori Aida, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda and Tomoyasu Sugiyama

Biomolecules 2020, 10(6), 931; https://doi.org/10.3390/biom10060931 - 19 Jun 2020

Cited by 24 | Viewed by 5870

Abstract

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. [...] Read more.

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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8 pages, 2342 KiB

Open AccessArticle

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

by Kenya Kusunose, Akihiro Haga, Mizuki Inoue, Daiju Fukuda, Hirotsugu Yamada and Masataka Sata

Biomolecules 2020, 10(5), 665; https://doi.org/10.3390/biom10050665 - 25 Apr 2020

Cited by 42 | Viewed by 4009

Abstract

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an [...] Read more.

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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18 pages, 2889 KiB

Open AccessArticle

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

by Ken Asada, Kazuma Kobayashi, Samuel Joutard, Masashi Tubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese and Ryuji Hamamoto

Biomolecules 2020, 10(4), 524; https://doi.org/10.3390/biom10040524 - 30 Mar 2020

Cited by 32 | Viewed by 5161

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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14 pages, 2450 KiB

Open AccessArticle

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

by Ryo Kanada, Atsushi Tokuhisa, Koji Tsuda, Yasushi Okuno and Kei Terayama

Biomolecules 2020, 10(3), 482; https://doi.org/10.3390/biom10030482 - 21 Mar 2020

Cited by 5 | Viewed by 3868

Abstract

Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in [...] Read more.

Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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17 pages, 17185 KiB

Open AccessArticle

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

by Yoshihisa Tanaka, Yoshinori Tamada, Marie Ikeguchi, Fumiyoshi Yamashita and Yasushi Okuno

Biomolecules 2020, 10(2), 306; https://doi.org/10.3390/biom10020306 - 14 Feb 2020

Cited by 8 | Viewed by 5155

Abstract

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and [...] Read more.

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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12 pages, 2159 KiB

Open AccessEditor’s ChoiceArticle

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

by Jun Akatsuka, Yoichiro Yamamoto, Tetsuro Sekine, Yasushi Numata, Hiromu Morikawa, Kotaro Tsutsumi, Masato Yanagi, Yuki Endo, Hayato Takeda, Tatsuro Hayashi, Masao Ueki, Gen Tamiya, Ichiro Maeda, Manabu Fukumoto, Akira Shimizu, Toyonori Tsuzuki, Go Kimura and Yukihiro Kondo

Biomolecules 2019, 9(11), 673; https://doi.org/10.3390/biom9110673 - 30 Oct 2019

Cited by 20 | Viewed by 6501

Abstract

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images [...] Read more.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87–0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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Review

Jump to: Editorial, Research

19 pages, 3338 KiB

Open AccessReview

The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding

by Jason Lin and Hiroki Nagase

Biomolecules 2020, 10(4), 544; https://doi.org/10.3390/biom10040544 - 03 Apr 2020

Cited by 7 | Viewed by 4088

Abstract

The high sequence specificity of minor groove-binding N-methylpyrrole-N-methylimidazole polyamides have made significant advances in cancer and disease biology, yet there have been few comprehensive reports on their off-target effects, most likely as a consequence of the lack of available tools [...] Read more.

The high sequence specificity of minor groove-binding N-methylpyrrole-N-methylimidazole polyamides have made significant advances in cancer and disease biology, yet there have been few comprehensive reports on their off-target effects, most likely as a consequence of the lack of available tools in evaluating genomic binding, an essential aspect that has gone seriously underexplored. Compared to other N-heterocycles, the off-target effects of these polyamides and their specificity for the DNA minor groove and primary base pair recognition require the development of new analytical methods, which are missing in the field today. This review aims to highlight the current progress in deciphering the off-target effects of these N-heterocyclic molecules and suggests new ways that next-generating sequencing can be used in addressing off-target effects. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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21 pages, 1969 KiB

Open AccessFeature PaperReview

Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

by Ryuji Hamamoto, Masaaki Komatsu, Ken Takasawa, Ken Asada and Syuzo Kaneko

Biomolecules 2020, 10(1), 62; https://doi.org/10.3390/biom10010062 - 30 Dec 2019

Cited by 58 | Viewed by 9158

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)

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