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scDrug: From single-cell RNA-seq to drug response prediction

computational and Structural Biotechnology Journal 2023年 21卷 150-157页

作者： Hsieh, Chiao-Yu Wen, Jian-Hung Lin, Shih-Ming Tseng, Tzu-Yang Huang, Jia-Hsin Huang, Hsuan-Cheng Juan, Hsueh-Fen Taiwan AI Labs Taipei10351 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan Department of Life Science National Taiwan University Taipei10617 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei10617 Taiwan

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://***/ailabstw/scDrug. © 2022 The Authors

关键词： RNA

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Deterministic identity testing paradigms for bounded top-fanin depth-4 circuits 21

Deterministic identity testing paradigms for bounded top-fan...

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36th computational Complexity Conference, CCC 2021

作者： Dutta, Pranjal Dwivedi, Prateek Saxena, Nitin Chennai Mathematical Institute India Department of Computer Science and Engineering IIT Kanpur India

ISBN: (纸本)9783959771931

Polynomial Identity Testing (PIT) is a fundamental computational problem. The famous depth-4 reduction (Agrawal & Vinay, FOCS'08) has made PIT for depth-4 circuits, an enticing pursuit. The largely open special-cases of sum-product-of-sum-of-univariates (Σ[k]ΠΣ∧) and sum-product-of-constant-degree-polynomials (Σ[k]ΠΣΠ[δ]), for constants k, δ, have been a source of many great ideas in the last two decades. For eg. depth-3 ideas (Dvir & Shpilka, STOC'05;Kayal & Saxena, CCC'06;Saxena & Seshadhri, FOCS'10, STOC'11);depth-4 ideas (Beecken,Mittmann & Saxena, ICALP'11;Saha,Saxena & Saptharishi, ***.'13;Forbes, FOCS'15;Kumar & Saraf, CCC'16);geometric Sylvester-Gallai ideas (Kayal & Saraf, FOCS'09;Shpilka, STOC'19;Peleg & Shpilka, CCC'20, STOC'21). We solve two of the basic underlying open problems in this work. We give the first polynomial-time PIT for Σ[k]ΠΣ∧. Further, we give the first quasipolynomial time blackbox PIT for both Σ[k]ΠΣ∧ and Σ[k]ΠΣΠ[δ]. No subexponential time algorithm was known prior to this work (even if k = δ = 3). A key technical ingredient in all the three algorithms is how the logarithmic derivative, and its power-series, modify the top Π-gate to ∧. © Pranjal Dutta, Prateek Dwivedi, and Nitin Saxena;licensed under Creative Commons License CC-BY 4.0

关键词： Timing circuits

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Mosquito Type Identification using Convolution Neural Network 3

Mosquito Type Identification using Convolution Neural Networ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Kumar, V Senthil Prasad, Veduri Veera Kunaisa, Khallik Swain, Mahendra Pratap Anandaram, Harishchander Kumar, Ashok Malla Reddy Institute of Technology and Science Department of Computer Science and Engineering Telangana Secunderabad500100 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem India Hkbk College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Department of Pharmaceutical Sciences and Technology Jharkhand Mesra Ranchi835215 India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore641112 India Banasthali Vidyapith Department of Computer Science Rajasthan Banasthali304022 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Mosquitoes are insects which are most commonly found in places with high water content. There are various types of mosquitos and each type has its own destructive characteristics. In order to completely eradicate mosquitoes from a particular place, people must know what type of mosquito it is. But this is not a task that can be done with naked eyes. To help this situation, this study develops a Deep Learning (DL) model which can predict the type of mosquito with great accuracy. For this purpose, a dataset of images of the Aedes and culex species of mosquito is collected from Kaggle. Following that, this dataset is split into three parts: training, validation, and testing. Then, pixel and image scaling is used to preprocess this dataset. A DL model is produced using the Convolutional Neural Network (CNN) technique. The model is then trained using the preprocessed dataset. The results of the training are also calculated and documented. The results are then put into a graph for easier examination. The highest accuracy of the model is produced during training and it is 99.2%. As for the loss values, the lowest loss value is 0.8% which is so small for a classification algorithm. Next, tests are conducted on the trained and validated models. A confusion matrix is then used to examine the model performance. The model is then assessed using the metrics such as accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), precision, and F1 score. These parameters' final values were also determined to be satisfactory. © 2022 IEEE.

关键词： Convolutional neural networks

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An Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern

An Efficient Prevention and Challenges in Wireless Sensor Ne...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Sumathy G K. Geetha Maheshwari A AR Arunarani Prithi Samuel Department of Computational Intelligence SRMIST Chennai India Department of Computer Science and Engineering Periyar Maniammai Institute of science and Technology Thanjavur

To have the good quality design of applications for Wireless Sensor Network (WSN), it's is much important for considering the challenges. WSN emerged with some common criteria with wireless network; they have the technical challenges, which is different from the traditional WSNs. The algorithm and protocol, which was proposed for traditional WSN, is not comparably good for the modern WSN. WSN has multiple type of sensors depending on the wide range of parameters. The deployment of nodes in WSN is in a random manner or the nodes will the manually planted in the desired location. The deployment of node in the areas where manual attending is difficult, there are chances of energy deficiency due to regular discharge of battery or by the short circuit. For minimizing these data loss issues, the affected nodes should be detected. The deployment of Sensor Node (SN) makes congestion in network by continuous transmissions by many SN. The other could be by the length of the link physically. Like, two nearby nodes cannot communicate with each other due to the physical interference (whereas distance nodes can be connected). The SN could have larger number of the sensor nodes which has the ability of sense, processing the data and commutation abilities. This paper describes the simulation tool for the implementation. Finally, the performance of Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern is provided. From the experimental results, security management system is estimated.

关键词： Wireless sensor networks Protocols Wireless networks Security management Manuals Interference Fault location

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

Investigation of Security Attacks in IoMT Devices and Federa...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Kiruthika, M. Poongodi, T. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Karnataka Bangalore India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients' data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research. © 2025 The Authors. Published by Elsevier B.V.

关键词： Federated learning

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Intervention of Digital Twin for Smart Monitoring of Battery Management System 14

Intervention of Digital Twin for Smart Monitoring of Battery...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Gupta, Amit Kaushik, Vishal Anandaram, Harishchander Gupta, Himanshu Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Coer University Roorkee India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

The use of digital twins (DT) has made important improvements not just from the beginning of the process of automobile development through the final phases of vehicle building, but additionally during its usage, collecting essential information from its everyday activities to make traveling more joyful, relaxed, and secure. This study organizes the DT development history, fundamental principles, and essential technologies, as well as summarizing current research methodologies and problems in battery simulation, state estimate, remaining useful life (RUL) projection, battery protection, and management. In addition, study provide methods for batteries digital simulation, real-time status calculation, variable charging supervision, dynamic heat leadership, and energetic leveling regulation in the smart battery management system (BMS) using DT. The study also provides prospects for DT growth in the industry of battery. Lastly, the article concludes with an examination of the technological economic effects of DT technology, which will revolutionize conventional automobile manufacturing, as well as the barriers to continued growth. © 2023 IEEE.

关键词： Blockchain

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 6

An Effective Implementation of Detection and Retrieval Prope...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Sharma, Aniket Singh, Pramod Kumar Prakash, Jay Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt's experiential memory model. A comparison of detection efficiency and accuracy and the proposed model's retrieval property with a recently suggested method demonstrate its effectiveness and superiority. © 2022 Association for Computing Machinery.

关键词： Long short-term memory

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TRANSFER LEARNING ON MULTI-DIMENSIONAL DATA: A NOVEL APPROACH TO NEURAL NETWORK-BASED SURROGATE MODELING

arXiv

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arXiv 2024年

作者： Propp, Adrienne M. Tartakovsky, Daniel M. Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States Department of Energy Science and Engineering Stanford University StanfordCA94305 United States

The development of efficient surrogates for partial differential equations (PDEs) is a critical step towards scalable modeling of complex, multiscale systems-of-systems. Convolutional neural networks (CNNs) have gained popularity as the basis for such surrogate models due to their success in capturing high-dimensional input-output mappings and the negligible cost of a forward pass. However, the high cost of generating training data — typically via classical numerical solvers — raises the question of whether these models are worth pursuing over more straightforward alternatives with well-established theoretical foundations, such as Monte Carlo methods. To reduce the cost of data generation, we propose training a CNN surrogate model on a mixture of numerical solutions to both the d-dimensional problem and its (d − 1)-dimensional approximation, taking advantage of the efficiency savings guaranteed by the curse of dimensionality. We demonstrate our approach on a multiphase flow test problem, using transfer learning to train a dense fully-convolutional encoder-decoder CNN on the two classes of data. Numerical results from a sample uncertainty quantification task demonstrate that our surrogate model outperforms Monte Carlo with several times the data generation budget. © 2024, CC BY.

关键词： Convolutional neural networks

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Sparsity-aware generalization theory for deep neural networks

arXiv

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arXiv 2023年

作者： Muthukumar, Ramchandran Sulam, Jeremias Department of Computer Science Mathematical Institute for Data Science Johns Hopkins University United States Department of Biomedical Engineering Mathematical Institute for Data Science Johns Hopkins University United States

Deep artificial neural networks achieve surprising generalization abilities that remain poorly understood. In this paper, we present a new approach to analyzing generalization for deep feed-forward ReLU networks that takes advantage of the degree of sparsity that is achieved in the hidden layer activations. By developing a framework that accounts for this reduced effective model size for each input sample, we are able to show fundamental trade-offs between sparsity and generalization. Importantly, our results make no strong assumptions about the degree of sparsity achieved by the model, and it improves over recent norm-based approaches. We illustrate our results numerically, demonstrating non-vacuous bounds when coupled with data-dependent priors in specific settings, even in over-parametrized models. © 2023, CC BY.

关键词： Deep neural networks

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