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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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Interpretable SincNet-Based Spatiotemporal Neural Network for Seizure Prediction 46

Interpretable SincNet-Based Spatiotemporal Neural Network fo...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Shan, Baolian Yu, Haiqing Jiang, Hanzhe Huang, Yongzhi Xu, Minpeng Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China

ISBN: (纸本)9798350371499

Spatiotemporal convolutional neural networks (CNNs) have emerged as potent tools for seizure prediction (SP) using electroencephalogram (EEG) signals, probing spatiotemporal biomarkers in epileptic brains. Nevertheless, it poses significant challenges for clinical practice due to the poor interpretability of learned features and the numerous trainable parameters in existing CNNs. To improve the interpretability and performance, this study proposed an interpretable SincNet-based architecture for spatiotemporal CNNs, encompassing EEGNet-8,2, ShallowConvNet, DeepConvNet, and EEGWaveNet, enabling direct visualization of the bandpass temporal filter range using a sinc-convolution layer. Furthermore, we also constructed a visualization analysis method to demonstrate the crucial spatiotemporal features learned by the proposed optimal CNN. Results on the CHB-MIT dataset revealed that both ShallowConvNet and EEGWaveNet had significantly improved performance with more lightweight parameters. Notably, the architecture enabled ShallowConvNet to achieve an average accuracy of 87.2%, sensitivity of 88.3%, weighted F1-score of 87.1%, and AUC of 92.7% for 21 epilepsy patients. Besides, the visualization outcomes underscored the ability of the optimal model to extract statistically significant spatiospectral energy differences within high-frequency EEG bands for SP classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-GPU GEMM Algorithm performance Analysis for Nvidia and AMD GPUs Connected by NVLink and PCIe 1

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22nd International conference on Mathematical Modeling and Supercomputer Technologies, MMST 2022

作者： Choi, Yea Rem Stegailov, Vladimir HSE University Moscow Russia Joint Institute for High Temperatures of RAS Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia

ISBN: (数字)9783031241451

ISBN: (纸本)9783031241444

Modern types of multi-GPU servers combine up to 8 A100 GPUs connected by NVLink 3.0 links through NVSwitch. This connectivity provides unprecedented capabilities for multi-GPU algorithms. In this work, we analyze the performance of matrix-matrix multiplication algorithm developed by us previously. Tuning principles and limits for maximum performance are discussed. Algorithm performance for much more affordable 4 AMD Radeon RX 6900 XT based server with PCI 4.0 working under ROCm HIP is described for comparison. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： program processors

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FPGA Design of RISC-V MCU Collaborative Industrial Printer Control System 6

FPGA Design of RISC-V MCU Collaborative Industrial Printer C...

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6th IEEE International conference on Knowledge Innovation and Invention, ICKII 2023

作者： Yen, Mao-Hsu Lin, Tzu-Feng Tsou, Cheng-Hao Yu, Yan-Wei Lin, Yih-Hsia Ku, Yuan-Fu Kao, Chien-Ting National Taiwan Ocean University Dept. of Computer Science and Engineering Keelung Taiwan Ming Chuan University Dept. of Electronic Engineering Taoyuan Taiwan Taiwan Testing and Certification Center Taoyuan Taiwan Dept. of Bureau of Standards Taipei Taiwan

ISBN: (纸本)9798350323535

The industrial printer control system was proposed in this research with a hardware architecture to support four print heads simultaneously. The system quickly and accurately calculated the printing distance and time of inkjet printing to achieve high-quality printing effects. In terms of vertical resolution, the system provided options such as 600, 300, 150, and 75 DPI. Horizontally, the system performed various DPI printing by detecting and calculating the speed of the rotary encoder. Following high-quality printing standards, the printing performance of industrial printers was enhanced, and the fastest printing speed reached 943 m/min. This industrial printer control system was implemented on an Altera FPGA incorporating the design of a reduced instruction set computer-V (RISC-V) microcontroller unit (MCU) [2] and an in-circuit debugger (ICD). The RISC-V MCU has a three-stage pipeline architecture to improve performance through parallel processing. The design was streamlined and modular to reduce costs, and an EBI BUS was added to communicate with peripheral devices. The ICD was used to monitor the status of the printing control system at any time and display the printing situation on the software platform. These comprehensive functions allowed industrial printers to have more complete functions and expandability. Using the SystemVerilog hardware description language, ModelSim's HDL simulation software was modified and tested. The functionality was verified through the field-programmable gate array (FPGA) program to ensure that it's correct. The implementation results can achieve the functions of an industrial printer control system with a speed of 100MHz. © 2023 IEEE.

关键词： computer hardware description languages

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SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network 38

SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Re...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Weiyu Sun, Ying Xu, Yijie Qiao, Ziyue Yang, Yongkui Xiong, Hui Thrust of Artificial Intelligence HKUST Guangzhou China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering HKUST Hong Kong

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) high Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/. © 2024 Neural information processing systems foundation. All rights reserved.

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DDMI: DOMAIN-AGNOSTIC LATENT DIFFUSION MODELS FOR SYNTHESIZING high-QUALITY IMPLICIT NEURAL REPRESENTATIONS 12

DDMI: DOMAIN-AGNOSTIC LATENT DIFFUSION MODELS FOR SYNTHESIZI...

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12th International conference on Learning Representations, ICLR 2024

作者： Park, Dogyun Kim, Sihyeon Lee, Sojin Kim, Hyunwoo J. Department of Computer Science Korea University Seoul Korea Republic of

Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains. These models opened the door for domain-agnostic generative models, but they often fail to achieve high-quality generation. We observed that the existing methods generate the weights of neural networks to parameterize INRs and evaluate the network with fixed positional embeddings (PEs). Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation. To address this limitation, we propose Domain-agnostic Latent Diffusion Model for INRs (DDMI) that generates adaptive positional embeddings instead of neural networks' weights. Specifically, we develop a Discrete-to-continuous space Variational AutoEncoder (D2C-VAE) that seamlessly connects discrete data and continuous signal functions in the shared latent space. Additionally, we introduce a novel conditioning mechanism for evaluating INRs with the hierarchically decomposed PEs to further enhance expressive power. Extensive experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance Fields, and videos, with seven benchmark datasets, demonstrate the versatility of DDMI and its superior performance compared to the existing INR generative models. Code is available at https://***/mlvlab/DDMI. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Embeddings

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Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Improving Medical Image Denoising via a Lightweight Plug-and...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ma, Lei Kuang, Hulin Liu, Jin Shen, Chengchao Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP2M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP2M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP2M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising. © 2023 IEEE.

关键词： adaptive receptive field selection high-low frequency processing image receptor medical image denoising plug-and-play

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Data-Driven Modeling of IoT-based Smart Buildings for Energy Prediction

Data-Driven Modeling of IoT-based Smart Buildings for Energy...

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2024 ASHRAE Winter conference

作者： Xu, Dikai Shin, Jaewoo Xu, Kayla Grama, Ananth Qu, Ming Lyles School of Civil Engineering Purdue University West LafayetteIN United States Department of Computer Science Purdue University West LafayetteIN United States West Lafayette Jr./Sr. High School West LafayetteIN United States Department of Computer Science Purdue University West LafayetteIN United States Lyles School of Civil Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9781955516822

Building environments are highly stochastic and influenced by outdoor weather conditions, operating schedules, internal thermal gains, and occupant behavior, to name a few. Traditional physics-based models require elaborate inputs specifying detailed building designs and air-flow systems, while statistical models are often less accurate and limited to the facility and conditions under which the models were constructed. Both types of models are context-specific and time-consuming, making them unsuitable for supporting energy analysis or intelligent control of complex systems with massive amounts of noisy data. With the increasing application of Internet of Things (IoT)-based sensing and controls in buildings, large volumes of building data have become available in the cloud. This highlight motivates the urgent need for robust data-driven models using machine learning (ML) techniques to facilitate energy analytics and prediction in IoT-based smart buildings. The primary objective of the study is to develop a robust data-driven model for building energy analytics and optimization. To achieve this goal, a case study was performed on an operational IoT-based building with real-time sensed data. Models based on Support Vector Machine (SVM), Feedforward Neural Networks (FNN), and Recurrent Neural Network (RNN) were developed to relate high-throughput streaming data to observable parameters of interest, learn from the operational patterns of the building response, and make predictions about future energy consumption. An automated ML pipeline was established consisting of streaming data collection from the cloud, data preprocessing, feature selection, feature engineering, model training, and validation. The model was specifically used to forecast the building energy performance. The data-driven model has been proven to be highly accurate in predicting energy consumption, thereby enabling optimal energy consumption and enhancing the overall performance of the building. © 2024

关键词： Energy utilization

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A Vision-based Indoor Positioning Systems utilizing computer Aided Design Drawing 22

A Vision-based Indoor Positioning Systems utilizing Computer...

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28th Annual International conference on Mobile Computing and Networking (ACM MobiCom)

作者： Yoo, Dae-ha Shan, Gaoyang Roh, Byeong-hHee Ajou Univ Dept AI Convergence Network Suwon South Korea Ajou Univ Inst Informat & Commun Suwon South Korea

ISBN: (纸本)9781450391818

In recent years, with the increase in users' demand for location services, the research of Indoor Positioning Systems (IPS) has attracted much attention. Many researchers proposed schemes to estimate the user's location based on the Received Signal Strength Indicator (RSSI) values of wireless technologies. However, the RSSI value is affected by signal interference seriously. This causes the accuracy of the positioning to be greatly reduced. To solve this problem, we use computer Vision (CV) to replace traditional solutions in this paper. CV is known for its high performance and low complexity. The proposed scheme is capable of inferring the current location of users in the possible candidates from the interior structural features of buildings captured by cameras and computer-Aided Design (CAD) drawing.

关键词： Indoor Positioning System Artificial Intelligence Vision and Scene Understanding

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Channel Modeling and performance Analysis of RIS-Assisted MIMO Communications in high-Speed Rail Tunnels 9

Channel Modeling and Performance Analysis of RIS-Assisted MI...

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9th International conference on computer and Communications, ICCC 2023

作者： Shao, Yulong Wang, Hailong Wang, Dongming Song, Tiecheng Chen, Jianping Southeast University Nanjing China Ticom Tech Nanjing China

ISBN: (纸本)9798350317251

As a typical application scenario of the fifth-generation(5G) mobile communication system, the high-speed mobile scenario attracts much attention and the tunnel is a typical application scenario for high-speed rail communication. As one of the key beyond 5G(B5G) technologies with the greatest development, Reconfigurable Intelligence Surface(RIS) has the advantages of low complexity, low cost, and easy deployment, providing new opportunities for the development of communications in 5G smart high-speed rail tunnel scenarios. The traditional high-speed rail communication in the tunnel is covered by leaky coaxial cables. The spectrum efficiency of the communication system covered by leaky coaxial cables is limited by deployment and is difficult to significantly improve. Based on the fixed scatterer distribution characteristics in the tunnel and considering the angular relationship between the transmitter, the reflecting surface, and the receiver, the deployment position and quantity of RIS are designed. The paper considers deploying RIS at the top inside the tunnel, thereby establishing a single-bounced channel model for RIS-assisted wireless communication. The model considers the direct path from the transmitter to the receiver and the assisted path provided by the RIS, supporting the deployment of multiple RIS relays. Under this model, the paper carries out a simulation evaluation for RIS-assisted MIMO communication. The simulation results show that the introduction of RIS can effectively expand coverage and demonstrate that the large size and appropriate location of the RIS can significantly improve the channel capacity. © 2023 IEEE.

关键词： 5G mobile communication systems

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