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Machine learning in the prediction of cancer therapy

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2021年 19卷 4003-4017页

作者： Rafique, Raihan Islam, S. M. Riazul Kazi, Julhash U. Ideflod AB Lund Sweden Sejong Univ Dept Comp Sci & Engn Seoul South Korea Lund Univ Div Translat Canc Res Dept Lab Med Medicon Village Bldg 404 C3Scheelevagen 8 S-22363 Lund Sweden Lund Univ Dept Lab Med Lund Stem Cell Ctr Lund Sweden

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Artificial intelligence Deep learning Monotherapy prediction Drug combinations Drug synergy variational autoencoder Restricted Boltzmann machine Support vector machines Ridge regression Elastic net Lasso Random forests Deep neural network Convolutional neural network Graph convolutional network Matrix factorization Factorization machine Higher-order factorization machines Visible neural network Ordinary differential equation

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CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method

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BMC BIOINFORMATICS 2021年第1期22卷 1-21页

作者： Zhang, Leili Domeniconi, Giacomo Yang, Chih-Chieh Kang, Seung-gu Zhou, Ruhong Cong, Guojing IBM Thomas J Watson Res Ctr 1101 Kitchawan Rd Yorktown Hts NY 10598 USA Zhejiang Univ 688 Yuhangtang Rd Hangzhou 310027 Peoples R China Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37830 USA

Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

关键词： Lead optimization Drug discovery Molecular dynamics simulation Machine learning variational autoencoder Clustering

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A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

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IEEE ACCESS 2021年 9卷 58838-58851页

作者： Karamti, Hanen Lashin, Maha M. A. Alrowais, Fadwa M. Mahmoud, Abeer M. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Comp Sci Dept Riyadh 11564 Saudi Arabia Univ Sfax MIRACL Lab Higher Inst Comp Sci & Multimedia Sfax ISIMS Sfax 3029 Tunisia Princess Nourah Bint Abdulrahman Univ Coll Engn Riyadh 11564 Saudi Arabia Benha Univ Fac Engn Shoubra Mech Engn Dept Banha 13518 Egypt Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo 11566 Egypt

Effective intelligent fault diagnosis of rotating machinery using its vibrational signals has a considerable influence on certain analysis factors such as the reliability, performance, and productivity of a variety of modern manufacturing machines. Traditional intelligent approaches lack generalization schemes and add the burden of extracting features from data-driven cases. On the other hand, the Deep Learning (DL) studies have reported capabilities higher than the expectations of the researchers' objectives. In this context, this paper proposes a new deep architecture based on Stacked Variant autoencoders for multi-fault machinery identification with imbalanced samples. The proposed model starts with a variational autoencoder (VAE) for facilitating data augmentation of small and imbalanced data samples using Gaussian distribution. After the preparation of suitable samples based on quality and size, the preprocessed vibration signals obtained are injected into the deep framework. The proposed deep architecture contains two subsequent unsupervised Sparse autoencoders (SAE) with a penalty term that helps in acquiring more abstract and essential features as well as avoiding redundancy. The output of the second SAE is integrated on a supervised Logistic Regression (LR) with 10 classes. This is utilized for the proposed classifier training to achieve accurate fault identification. Experimental results show the efficiency of the proposed model which achieved an accuracy of 93.2%. In addition, for extensive comparative analysis issue, the Generative Adversarial Network (GAN) and triNetwork Generative Adversarial Network (tnGAN) were both implemented on the vibrational signal data, where the proposed method reported better results in terms of training and testing time and overall accuracy.

关键词： Feature extraction Vibrations Deep learning Fault diagnosis Data models Training Principal component analysis Fault diagnosis imbalanced samples logistic regression rotating machinery sparse autoencoders variational autoencoder vibrational signals

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Sliding window-based LightGBM model for electric load forecasting using anomaly repair

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JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 12857-12878页

作者： Park, Sungwoo Jung, Seungmin Jung, Seungwon Rho, Seungmin Hwang, Eenjun Korea Univ Sch Elect Engn Seoul South Korea Chung Ang Univ Dept Ind Secur Seoul South Korea

Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can lead to inefficient operation if the meter data are damaged or tampered with during collection or transmission. Therefore, it is important to identify abnormalities in smart grid data and process them accordingly. Various anomaly detection models have been proposed using statistical methods, but they cannot detect some anomaly patterns accurately, and the models generally did not consider repair strategies for the detected anomalies. Anomaly repair should be included with model training to improve forecasting performance. This paper proposes a robust sliding window-based LightGBM model for short-term load forecasting using anomaly detection and repair. We first show how to detect anomalies using a variational autoencoder and then how they can be repaired using a random forest method. Finally, we verify that the proposed sliding window-based LightGBM achieves superior forecasting performance in combination with anomaly detection and repair.

关键词： Anomaly detection Data repair Electric load forecasting variational autoencoder Random forest LightGBM Sliding window

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A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images

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REMOTE SENSING 2024年第5期16卷 781-781页

作者： Abady, Lydia Dimitri, Giovanna Maria Barni, Mauro Univ Siena Dept Informat Engn & Math I-53100 Siena Italy

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers generally achieve high detection accuracies, they struggle to generalize to image domains and generative architectures different from those encountered during training. In this paper, we propose a one-class classifier based on Vector Quantized variational autoencoder 2 (VQ-VAE 2) features to overcome the limitations of two-class classifiers. We start by highlighting the generalization problem faced by binary classifiers. This was demonstrated by training and testing an EfficientNet-B4 architecture on multiple multi-spectral datasets. We then illustrate that the VQ-VAE 2-based classifier, which was trained exclusively on pristine images, could detect images from different domains and generated by architectures not encountered during training. Finally, we conducted a head-to-head comparison between the two classifiers on the same generated datasets, emphasizing the superior generalization capabilities of the VQ-VAE 2-based detector, wherewe obtained a probability of detection at a 0.05 false alarm rate of 1 for the blue and red channels when using the VQ-VAE 2-based detector, and 0.72 when we used the EfficientNet-B4 classifier.

关键词： generative adversarial networks variational autoencoder EfficientNet detection Sentinel-2 remote sensing

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Small time asymptotics of the entropy of the heat kernel on a Riemannian manifold

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APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2024年 71卷

作者： Menkovski, Vlado Portegies, Jacobus W. Ravelonanosy, Mahefa Ratsisetraina Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands Eindhoven Univ Technol EAISI Eindhoven Netherlands Eindhoven Univ Technol Dept Math & Comp Sci POB 513 NL-5600 MB Eindhoven Netherlands

We give an asymptotic expansion of the relative entropy between the heat kernel q(Z)(t, z, w) of a compact Riemannian manifold Z and the normalized Riemannian volume for small values of t and for a fixed element z is an element of Z. We prove that coefficients in the expansion can be expressed as universal polynomials in the components of the curvature tensor and its covariant derivatives at z, when they are expressed in terms of normal coordinates. We describe a method to compute the coefficients, and we use the method to compute the first three coefficients. The asymptotic expansion is necessary for an unsupervised machine-learning algorithm called the Diffusion variational autoencoder.

关键词： Kullback-Leibler divergence Entropy Heat kernel Parametrix expansion variational autoencoder

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User preference-aware content caching strategy for video delivery in cache-enabled IoT networks

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COMPUTER NETWORKS 2024年 240卷

作者： Firouzjaee, Mostafa Taghizade Jamshidi, Kamal Moghim, Neda Shetty, Sachin Univ Isfahan Dept Comp Engn Esfahan *** Iran Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to counteract this traffic upswing. Caching not only improves network efficiency but also enhances user service quality. Critical to the development of an optimal caching algorithm is the accurate prediction of future content popularity. This prediction hinges on the ability to anticipate users' content preferences, which is a pivotal method for assessing content popularity. In this study, we introduce a novel caching strategy termed User Preference-aware content Caching Strategy (UPCS) tailored for an IoT platform, where users access multimedia services offered by remote Content Providers (CPs). The UPCS encompasses three key algorithms: a content popularity prediction algorithm that utilizes variational autoencoders (VAE) to forecast users' future content preferences based on their prior requests, an online algorithm for dynamic cached content replacement, and a cooperative caching algorithm to augment caching efficiency. The proposed content caching strategy outperforms alternative methods, exhibiting superior cache hit rates and reduced Content Retrieval Delays (CRD).

关键词： Content caching Cooperative caching IoT Networks Popularity prediction Deep learning variational autoencoder

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Deep variational magnetic resonance image denoising via network conditioning

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Aetesam, Hazique Maji, Suman Kumar Birla Inst Technol Mesra Dept Comp Sci & Engn Patna 800014 India Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801106 India

In this paper, we propose a variational approach towards denoising magnetic resonance images (MRI) corrupted by spatially variant and signal-dependent Rician noise in a deep learning framework. To obtain a mathematically sound inference network, approximate variational posteriors are designed keeping in mind the Rician nature of noise. The proposed work tackles the denoising problem in several different ways. Firstly , the prior assumption on data in the variational posterior is motivated by the heavy-tailed marginal distribution of image gradients in natural images. This is captured by the sparsity promoting hyper-Laplacian prior on MR data. Similarly, median absolute deviation under Gaussian prior helps in the estimation of noise in the variational lower bound of marginal log likelihood term. Secondly , noise estimation from the background regions of the noisy data under the assumption of Rayleigh distribution prevents the addition of extra subnetwork for the estimation of spatially variant noise level parameters. Thirdly , feature-wise transformation of intermediate layers is performed using anatomical planes segmentation maps (APSM) for context-based network conditioning. Here, affine transformation parameters generated from APSM are modulated with the input features for spatial feature transformation. Fourthly , to capture the long-range dependencies lost in deeper convolutional layers, multiscale global feature fusion block (GFFuB) is used. Lastly , experimental results over synthetically corrupted MR data and real data obtained from MR scanners suggest the potential utility of the proposed model in real time.

关键词： Magnetic resonance imaging Noise estimation Rician distribution variational autoencoder Network conditioning

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Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials

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ADVANCED MATERIALS 2024年第6期36卷 2303481-2303481页

作者： Pahlavani, Helda Tsifoutis-Kazolis, Kostas Saldivar, Mauricio C. Mody, Prerak Zhou, Jie Mirzaali, Mohammad J. Zadpoor, Amir A. Delft Univ Technol TU Delft Fac Mech Maritime & Mat Engn Dept Biomech Engn Mekelweg 2 NL-2628 CD Delft Netherlands Leiden Univ Med Ctr LUMC Div Image Proc LKEB Radiol Albinusdreef 2 NL-2333 ZA Leiden Netherlands

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem. Mechanical metamaterials are architected engineered materials whose unprecedented properties originate from their designs at the microscale. Here, deep-learning techniques are utilized to develop a modular approach titled "Deep-DRAM" that generates numerous microstructures for mechanical metamaterials with predefined elastic properties and dimensions for various applications in high-tech industries, such as soft robotics and biomedical ***

关键词： additive manufacturing deep learning numerical simulations random-network mechanical metamaterials size-agnostic variational autoencoder

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Feature Extraction Based on Self-Supervised Learning for Remaining Useful Life Prediction

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第2期24卷 021004-021004页

作者： Yu, Zhenjun Lei, Ningbo Mo, Yu Xu, Xin Li, Xiu Huang, Biqing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Shenzhen Int Grad Sch Div Informat Sci Shenzhen 518055 Peoples R China China Nucl Power Engn Co Ltd Beijing 100840 Peoples R China

The prediction of the remaining useful life (RUL) is of great significance to ensure the safe operation of industrial equipment and to reduce the cost of regular preventive maintenance. However, the complex operating conditions and various fault modes make it difficult to extract features containing more degradation information with existing prediction methods. We propose a self-supervised learning method based on variational automatic encoder (VAE) to extract features of data's operating conditions and fault modes. Then the clustering algorithm is applied to the extracted features to divide data from different failure modes into different categories and reduce the impact of complex working conditions and fault modes on the estimation accuracy. In order to verify the effectiveness of the proposed method, we conduct experiments with different network structures on the C-MAPSS dataset, and the results verified that our method can effectively improve the feature extraction capability of the model. In addition, the experimental results further demonstrate the superiority and necessity of using hidden features for clustering rather than raw data.

关键词： fault prognosis remaining useful life prediction self-supervised learning variational autoencoder

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