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PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network

SN Computer science 2023年第5期4卷 1-13页

作者： Surana, Shraddha Arora, Pooja Singh, Divye Sahasrabuddhe, Deepti Valadi, Jayaraman Engineering for Research Thoughtworks Technologies Maharashtra Pune 411006 India Department of Chemical Engineering Indian Institute of Technology Maharashtra Bombay 400076 India Faculty of Computing and Data Science FLAME University Maharashtra Pune 411007 India

The continuous increase in pathogenic viruses and the intensive laboratory research emphasizes the need for cost- and time-efficient drug development. This accelerates research for alternate drug candidates like antiviral peptides (AVP) that have therapeutic and prophylactic potential and gaining attention in recent times. However, diversity in their sequences, limited and non-uniform characterization often limit their applications. Isolating newer peptide backbones with required characteristics is a cumbersome process with many design–test–build cycles. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful to expedite the initial stage of developing novel peptide drugs. In this study, we developed PandoraGAN that uses a manually curated training dataset of 130 highly active peptides that include peptides from known databases (such as AVPdb) and literature to generate novel antiviral peptides. The underlying architecture in PandoraGAN is able to learn a good representation of the implicit properties of antiviral peptides. The generated sequences from PandoraGAN are validated based on physico-chemical properties. They are also compared with the training dataset statistically using Pearson’s correlation and Mann–Whitney U-test. We, therefore, confirm that PandoraGAN is capable of generating a novel antiviral peptide backbone showing similar properties to that of the known highly active antiviral peptides. This approach exhibits a potential to discover novel patterns of AVP which may have not been seen earlier with traditional methods. To our knowledge, this is the first ever use of GAN models for antiviral peptides across the viral spectrum. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Antiviral peptide Drug discovery GAN Generative models Peptide therapeutics

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Expanding the Vocabulary of BERT for Knowledge Base Construction

arXiv

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arXiv 2023年

作者： Yang, Dong Wang, Xu Celebi, Remzi Institute of Data Science Department of Advanced Computing Sciences Maastricht University Netherlands

Knowledge base construction entails acquiring structured information to create a knowledge base of factual and relational data, facilitating question answering, information retrieval, and semantic understanding. The challenge called "Knowledge Base Construction from Pretrained Language Models" at International Semantic Web Conference 2023 defines tasks focused on constructing knowledge base using language model. Our focus was on Track 1 of the challenge, where the parameters are constrained to a maximum of 1 billion, and the inclusion of entity descriptions within the prompt is prohibited. Although the masked language model offers sufficient flexibility to extend its vocabulary, it is not inherently designed for multi-token prediction. To address this, we present Vocabulary Expandable BERT for knowledge base construction, which expand the language model’s vocabulary while preserving semantic embeddings for newly added words. We adopt task-specific re-pre-training on masked language model to further enhance the language model. Through experimentation, the results show the effectiveness of our approaches. Our framework achieves F1 score of 0.323 on the hidden test set and 0.362 on the validation set, both data set is provided by the challenge. Notably, our framework adopts a lightweight language model (BERT-base, 0.13 billion parameters) and surpasses the model using prompts directly on large language model (Chatgpt-3, 175 billion parameters). Besides, Token-Recode achieves comparable performances as Re-pretrain. This research advances language understanding models by enabling the direct embedding of multi-token entities, signifying a substantial step forward in link prediction task in knowledge graph and metadata completion in data management. 1MSC Codes 68T20 Copyright © 2023, The Authors. All rights reserved.

关键词： Information management

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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An Endless Outpainting Tool Based on Stable Diffusion

An Endless Outpainting Tool Based on Stable Diffusion

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Peter Garrity Leonidas Deligiannidis School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

We developed a tool that utilizes the capabilities of machine learning to explore the possibilities of image outpainting. Our tool is built around an image generation model based on Stable Diffusion and provides an easy-to-use command-based interface. The command-line interface allows us to generate an endless set of images. It starts with a given image, and after the outpainting process, it feeds the generated image as input to the next iteration. To preserve the original image's dimensions, before feeding an image as input to the next iteration, we scale it down so that the generated images are the same dimensions as the original image. By using several iterations like this, we can repeatedly create new details around the edge of an input image.

关键词： Scientific computing Image synthesis Image edge detection Machine learning Feeds Computational intelligence

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Signs to Speech

Signs to Speech

引用

Networking and Communications (ICNWC), International Conference on

作者： Harsh Vardhan Harsavardhan S M Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings.

关键词： Training Performance evaluation Sign language Adaptation models Refining Speech recognition Medical services

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Phylogenetic-Informed Generative Adversarial Network for Predicting Mutations in SARS-CoV-2

Phylogenetic-Informed Generative Adversarial Network for Pre...

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Networking and Communications (ICNWC), International Conference on

作者： Vijay Chandar Vishnu G Nair Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In the pursuit of developing more effective vaccines against viruses like SARS-CoV-2, understanding their potential mutations is crucial. In order to generate protein sequences, this study presents a unique framework that synergistically blends phylogenetic data with Generative Adversarial Networks (GANs). The proposed method combines the strengths of two models: TEMPO, a transformer-based model proficient in leveraging phylogenetic data for mutation detection in SARS-CoV-2, and MutaGAN, a GAN-based model adept in generating synthetic protein sequences. The phylogenetic data from TEMPO is used in this novel design as the input for the MutaGAN generator, replacing the conventional random noise input used in GANs. The generator may now create synthetic protein sequences that are impacted by the evolutionary past included in the phylogenetic data resulting in the creation of artificial protein sequences that are more biologically significant and lifelike. The discriminator portion of the model, similar to MutaGAN, is still in charge of determining whether or not the input sequences comprise a valid parent-child pair and uses sequences produced by the first GAN as extra negative cases. This prevented the model from going too far off its planned route. Additional investigation into this fascinating confluence of bioinformatics and AI may be possible as a result of this study’s conclusions. This approach has tremendous promise in many applications, such as the production of more powerful vaccinations, protein engineering, medication design, and improving our knowledge of biological systems, by forecasting how SARS-CoV-2 could change. A precision of 0.662 and an accuracy of around 0.671, together with a Matthews Correlation Coefficient (MCC) score of 0.313, show that the framework performs very well. These scores show that the framework performs better than a number of cutting-edge baseline techniques. The synthetic protein sequences generated by the framework not only bea

关键词： Proteins Protein engineering Biological system modeling Generative adversarial networks Transformers data models Generators

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Attentive Feature Segmentation model for Stroke detection on MRI images

Attentive Feature Segmentation model for Stroke detection on...

引用

BIT Conference (BITCON), International

作者： S.E. Viswapriya D. Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Medical image segmentation plays a crucial role in treatment planning, disease diagnosis, and guiding surgical navigation. Research should focus on medical image segmentation to achieve substantial breakthroughs or fill knowledge gaps that could lead to discoveries and improvements. Deep learning (DL) techniques have recently become necessary in creating advanced models, particularly for medical image segmentation. This paper introduces a novel segmentation algorithm specifically designed for MRI images. Initially, the paper will discuss the introduction and challenges in medical image segmentation. Later, developing a novel attentive feature-based segmentation algorithm is focused and their corresponding evaluation steps are measured. The evaluation metrics are finally compared with other existing segmentation algorithms and concluded that the proposed model surpasses the cutting-edge methods by notable differences in performance, showing the adaptability and overall effectiveness of the proposed model.

关键词： Deep learning Image segmentation Adaptation models Magnetic resonance imaging Semantic segmentation Surgery Computer architecture Feature extraction Medical diagnostic imaging Testing

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Exploring the Impact of Herbal Remedies on Autism Spectrum Disorder Using AGLSTM Analysis

Exploring the Impact of Herbal Remedies on Autism Spectrum D...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Bagiyalakshmi N T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur

Autism is a personality disorder that can cause significant emotional, intellectual, and behavioral deficits. Autism is recognized as an illness on the spectrum because, while certain kids might have mild symptoms, others may have extensive life-challenging abnormalities. However, autistic people have unique characteristics and health goals and may react differently to conventional or herbal therapy than others. Herbal medications might be a possible approach to helping autistic people's bodies and brains. However, further research is needed to determine whether herbal therapy is useful for autistic individuals. To gain insight into how people think regarding herbal medication for autism, data must be processed promptly as it is generated, which may be accomplished using sentiment assessment, which analyzes polarization in writings. This paper proposes the AGLSTM Model, a hybrid model that solves the sentiment evaluation issue by combining LSTM with agglomerative clustering. The suggested system signifies text data into numerical vectors that illustrate text features such as the frequency of words and embeddings of phrases using the TF-IDF technique and the word2vec model. Every word becomes assigned to a cluster using a method known as clustering based on how similar it is to the cluster nucleus or other texts in the region. The outcome of the experiment is a set of clusters composed of sentences with comparable emotional content. The LSTM framework can detect long-term correlations between word patterns and classify feedback as positive or negative.

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and Applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Anhui Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Attention mechanism Convolutional neural network Edge information Medical image segmentation Transformer

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Explaining Grover's Quantum Algorithm to College Students

Explaining Grover's Quantum Algorithm to College Students

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Computer science, Computer Engineering, & Applied computing (CSCE), Congress in

作者： Leonidas Deligiannidis School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

Wentworth institute of Technology is a baccalaureate degree granting institution and in recent years we have also developed several Master's programs. Our department offers several degrees leading to a Bachelor of science in Computer science, Computer Networking, Information Technology, Cybersecurity, and data science. All our programs are designed to be hands-on and expose students to a wide skill set. All our majors need to take several security-related courses. One of the latest security related courses we developed is in Quantum computing with a focus on security. Quantum computing is a groundbreaking technology that exploits the laws and properties of quantum mechanics. It can dramatically speed up computations of certain problems and surpass the computation power of modern supercomputers. One notable quantum algorithm we cover in our newly developed course is Grover's algorithm. It is used to search an unstructured database while providing quadratic speedup, compared to its classical counterpart. While demonstrating Grover's algorithm in the classroom, the students raise many questions and explaining all the details of the algorithm is challenging. In this paper, we explain and illustrate the theoretical and implementational challenges and provide examples and solutions.

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