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AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

Journal of Advanced Research 2025年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Kong, Yanwen Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Software Jilin University Jilin Changchun 130012 China School of Life Sciences Jilin University Jilin Changchun 130012 China Department of Computer Science Columbia University 116th and Broadway New York City 10027 NY United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang 550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun 130103 China Changchun Wenli High School Jilin Changchun 130062 China

Introduction: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. Objectives: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. Methods: This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Results: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the −log(MIC) values on the benchmark dataset. Conclusion: Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is ess

关键词： Activity cliff (AC) AMPCliff Antimicrobial peptide (AMP) Benchmark Minimum inhibitory concentration (MIC)

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An AO-ADMM approach to constraining PARAFAC2 on all modes

arXiv

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arXiv 2021年

作者： Roald, Marie Schenker, Carla Calhoun, Vince D. Adalı, Tülay Bro, Rasmus Cohen, Jeremy E. Acar, Evrim Department of Data Science and Knowledge Discovery Simula Metropolitan Center for Digital Engineering Oslo Norway Faculty of Technology Art and Design Oslo Metropolitan University Oslo Norway Department of Psychology Georgia State University AtlantaGA United States Department of Computer Science and Electrical Engineering UMBC BaltimoreMD United States Department of Food Science University of Copenhagen Copenhagen Denmark Univ Lyon INSA-Lyon UCBL UJM-Saint Etienne CNRS Inserm CREATIS UMR 5220 U1206 VilleurbanneF-69100 France

Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have unaligned time profiles. The PARAFAC2 model has been successfully used to analyze such data by allowing the underlying factor matrices in one mode (i.e., the evolving mode) to change across slices. The traditional approach to fit a PARAFAC2 model is to use an alternating least squares-based algorithm, which handles the constant cross-product constraint of the PARAFAC2 model by implicitly estimating the evolving factor matrices. This approach makes imposing regularization on these factor matrices challenging. There is currently no algorithm to flexibly impose such regularization with general penalty functions and hard constraints. In order to address this challenge and to avoid the implicit estimation, in this paper, we propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM). With numerical experiments on simulated data, we show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art. We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted *** Codes 15A69, 90C26 © 2021, CC BY.

关键词： Matrix algebra

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Senzi: A sentiment analysis lexicon for the latinised Arabic (Arabizi) 12

Senzi: A sentiment analysis lexicon for the latinised Arabic...

引用

12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Tobaili, Taha Fernandez, Miriam Alani, Harith Sharafeddine, Sanaa Hajj, Hazem Glavaš, Goran Knowledge Media Institute Open University United Kingdom Department of Computer Science and Mathematics Lebanese American University Lebanon Department of Electrical and Computer Engineering American University of Beirut Lebanon Data and Web Science Group Universität Mannheim Germany

ISBN: (纸本)9789544520557

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Sentiment analysis

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VAE-GAN based zero-shot outlier detection 4

VAE-GAN based zero-shot outlier detection

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4th International Symposium on computer science and Intelligent Control, ISCSIC 2020

作者： Ibrahim, Bekkouch Imad Nicolae, Dragos Constantin Khan, Adil Ali, Syed Imran Khattak, Asad Machine Learning and Knowledge Representation Lab Institute of Data Science and AI Innopolis Tatarstan Russia Institutul de Cercetari Pentru Inteligenta Artificiala Mihai Draganescu Romania Department of Computer Engineering Kyung Hee University Yongin-si Korea Republic of College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets. © 2020 ACM.

关键词： Anomaly detection

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Design of vehicle automatic braking systems considering drivers' braking characteristics

引用

International Journal of Performability engineering 2019年第9期15卷 2338-2345页

作者： Gao, Zhenhai Sun, Tianjun Hassan, MuHammad Wang, Liupu College of Automotive Engineering Jilin University Changchun130022 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Cancer Hospital Attached Xinjiang Medical University Urumqi830000 China Department of Computer Science and Information Technology University of Malakand KP Pakistan

With the rapid development of advanced driver assistance systems (ADAS), an automatic braking system has become increasingly important when faced with complicated traffic. The conventional decision-making method for the braking system is focused on security but lacks consideration of drivers' characteristics, which generates feelings of fear and frustration. We propose an anthropomorphic braking method based on vehicle kinematic characteristics and dynamic theories to improve the original system and eliminate the sense of jerking. Furthermore, on the basis of the traditional time to collision (TTC) algorithm, we consider the random motion of the target vehicle and develop a dynamic time to collision (DTTC) algorithm to meet different drivers' braking requirements. Therefore, in this study, the braking model is optimized by a series of driving simulator and real-vehicle braking tests. Finally, the validity and feasibility of the method are verified through the simulations of different braking modes. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Advanced driver assistance systems

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Urban traffic dynamic rerouting framework: a DRL-based model with fog-cloud architecture

arXiv

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arXiv 2021年

作者： Du, Runjia Chen, Sikai Dong, Jiqian Chen, Tiantian Fu, Xiaowen Labi, Samuel Lyles School of Civil Engineering Purdue University West LafayetteIN47907 United States Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Knowledge Management and Innovation Research Centre Department of Industrial and System Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Past research and practice have demonstrated that dynamic rerouting framework is effective in mitigating urban traffic congestion and thereby improve urban travel efficiency. It has been suggested that dynamic rerouting could be facilitated using emerging technologies such as fog-computing which offer advantages of low-latency capabilities and information exchange between vehicles and roadway infrastructure. To address this question, this study proposes a two-stage model that combines GAQ (Graph Attention Network – Deep Q Learning) and EBkSP (Entropy Based k Shortest Path) using a fog-cloud architecture, to reroute vehicles in a dynamic urban environment and therefore to improve travel efficiency in terms of travel speed. First, GAQ analyzes the traffic conditions on each road and for each fog area, and then assigns a road index based on the information attention from both local and neighboring areas. Second, EBkSP assigns the route for each vehicle based on the vehicle priority (vehicle’s proximity to intended destination) and route popularity (route’s frequency of patronage). A case study experiment is carried out to investigate the efficacy of the proposed model. At the experiment’s model training stage, different methods are used to establish the vehicle priorities, and their impact on the results is assessed. Also, the proposed model is tested under various scenarios with different ratios of rerouting and background (non-rerouting) vehicles. The results demonstrate that vehicle rerouting using the proposed model can help attain higher speed and reduces possibility of severe congestion. This result suggests that the proposed model can be deployed by urban transportation agencies for dynamic rerouting and ultimately, to reduce urban traffic congestion. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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GEOM-GCN: Geometric graph convolutional networks

arXiv

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arXiv 2020年

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Lei, Yu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign United States Department of Computing Hong Kong Polytechnic University Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Preface

Communications in Computer and Information Science

引用

Communications in computer and Information science 2024年 2008卷 v-vi页

作者： Dhar, Suparna Goswami, Sanjay Dinesh Kumar, U. Bose, Indranil Dubey, Rameshwar Mazumdar, Chandan NSHM Institute of Computing & Analytics NSHM Knowledge Campus Kolkata Kolkata India NSHM Knowledge Campus Kolkata India Indian Institute of Management Bangalore Bengaluru India Indian Institute of Management Ahmedabad Gujarat Ahmedabad India Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

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StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Tummala, Pavan Agrawal, Shubham Kumar Jauhari, Aishwarya Kalra, Aman Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642±0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859±0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ±0.135 and 0.783 ±0.111, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Thermal performance optimization of microchannel heat sinks with triangle wave fin designs and various heat transfer fluids using GA/RSM/TOPSIS

引用

Case Studies in Thermal engineering 2025年 72卷

作者： Amr S. Abouzied Ali Basem Talib K. Ibrahim Ahmad Almadhor Khalid Almutairi Hind Albalawi Salem Alkhalaf Dilsora Abduvalieva Ibrahim Elbadawy Samah G. Babiker Department of Pharmaceutical Chemistry College of Pharmacy University of Hail Hail 81442 Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala 56001 Iraq Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Applied College Mechanical Engineering Technology University of Hafr Al Batin Hafr Al Batin Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan College of Engineering and Technology American University of the Middle East Kuwait Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan

Microchannel heat sinks (MHSs) stand out as a promising solution for dissipating heat in high-performance systems. The development and optimization of these systems are vital to meeting the growing demands of modern, power-dense electronics. The present study introduces a novel approach to enhance heat dissipation in microchannel systems. The design incorporates two sets of fins with a distinctive triangle wave pattern within the microchannels. The acquired results for the introduced fins were compared with the data of a non-finned MHS and a device with straight fins. The maximum temperature of the non-finned MHS was 307.222 K, while the incorporation of the mentioned fins reduced the temperature by approximately 2 K. This reduction is significant, as lower maximum temperatures improve the reliability and longevity of electronic components. Response surface methodology (RSM) was utilized to develop predictive models, reducing reliance on complex simulations and costly prototypes. The effects of fin folding angle ( θ ) and spacing from side walls ( S ) on thermo-hydraulic performance were systematically analyzed. Optimization was achieved by integrating genetic algorithms with the predictive models. Three distinct optimization targets (maximizing the Nusselt number, minimizing the pressure drop, and maximizing the device's overall efficiency) were considered, and three optimum designs were proposed for each target. Additionally, Pareto front analysis and TOPSIS evaluation provided a comprehensive multi-objective assessment. In RSM-based anticipation models, the R 2 values were 0.971 for the Nusselt number and 0.977 for pressure drop. The remarkable R 2 values indicated that the models for both responses had a high level of accuracy. Based on the results, heat sink design 3 (HSD3) with geometric parameters of θ = 97.785° and S = 70 μm was recommended for enhancing the overall performance of the MHS. By utilizing just two triangle wave fins configured according to th

关键词： TOPSIS and Pareto fronts Optimization Response surface methodology Microchannel heat sink design Triangle wave fins Genetic Algorithm

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