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SLC6A7 as a Novel Prognostic and Diagnostic Biomarker in the Ovarian Serous Cystadenocarcinoma by Bioinformatics Approaches

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Indian Journal of Gynecologic Oncology 2025年第2期23卷 1-11页

作者： Mahmoudabadi, Ali Ghaderi, Shiva Aldaghi, Tahmineh Fathi, Sina Nazari, Elham Department of Medical Genetics Faculty of Medicine Kerman University of Medical Sciences Kerman Iran Department of Computer Engineers Faculty of Engineering AL-Zahra University Tehran Iran Institute of Biophysics and Informatics First Faculty of Medicine Charles University Prague Czech Republic Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Proteomics Research Center Faculty of Paramedical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Ovarian serous cystadenocarcinoma (OSC), accounting for approximately 90% of ovarian cancer cases, poses a significant global health issue due to the lack of effective screening methods. SLC6A7, a member of the solute carrier family, has emerged as a potential biomarker in cancer research, though its role in OSC remains underexplored. RNA expression profiling was performed using data from ovarian cancer patients in the TCGA database. Differentially expressed genes (DEGs) were identified using the Limma package in R. Prognostic biomarkers, and a risk model was developed using the Cox proportional hazard regression model and Kaplan–Meier survival analysis. Molecular pathways, protein–protein interaction (PPI) networks, and clinical and disease data correlations were further analyzed. Key findings include the identification of 393 DEGs, with eight genes (AVP, C8B, NACAP1, NHLH2, PTTG2, RRH, UBE2NL, and SLC6A7) overlapping as prognostic markers. SLC6A7 exhibited prognostic relevance (hazard ratio [HR] = 1.32, 95% CI 1.10–1.58, P = 0.0027) and diagnostic potential (AUC = 0.854). Enrichment analysis linked these genes to histone modifications and fatty acid omega oxidation pathways. Combining DEGs and prognostic genes provides insights into potential biomarkers for OSC. These findings suggest the possibility of early diagnosis, targeted therapy, and improved patient outcomes. SLC6A7 shows promise as a prognostic and diagnostic biomarker in OSC, though further experimental validation is needed to confirm its clinical utility.

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Groups generated by matrices of finite order

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Semigroup Forum 2025年 1-15页

作者： Bien, Mai Hoang Huy, Trinh Quoc Sy, Nguyen Anh Thinh, Nguyen Phu Truong, Le Quang Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Department of Mathematics - Informatics Ho Chi Minh City University of Education Ho Chi Minh City Viet Nam

Let R be an associative ring with identity, 1≤κ≤ℵ0, and n, k be two positive integers. Denote by Mκ(R) the ring of all κ×κ square matrices over R if κ<ℵ0, and the ring of all column-finite infinite matrices over R if κ=ℵ0. The ring of all upper triangular matrices in Mκ(R) is denoted by Tκ(R). In this paper, we first establish the necessary and sufficient conditions for a matrix to be expressible as a product of matrices whose orders are divisors of k in Tκ(R). As an application, we then describe the subgroup of the general linear group of degree n over a field generated by matrices whose orders are divisors of k. Naturally, we also extend this study to the subgroup of the Vershik–Kerov group, that is, the group of infinite matrices with only finitely many nonzero entries below the main diagonal. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Generators of matrix groups Infinite matrix Matrix of finite order Product of matrices Triangular matrix

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Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Early Detection of Breast Cancer with Microcalcifications on...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ashraf Osman Ibrahim Hafia Mamoun Ismail Abuharaz Mohammed A Saleh Razan Alharith Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Depeartment of Computer Science Al Neelain University Khartoum Sudan DaTA Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia School of Computing and Artificial Intelligence Southwest Jiaotong University

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. Dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.

关键词： Deep learning Training Accuracy Biological system modeling Breast cancer Mammography Data models Convolutional neural networks Testing Overfitting

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VLM-Enabled Network Experimentation: Case Study of Chanel Capacity in System Limited by η-μ Fading

VLM-Enabled Network Experimentation: Case Study of Chanel Ca...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Dejan Milić Nenad Petrović Milan Jović Radiša Stefanović Mejdin Saliji Elida Suljović Faculty of Electronic Engineering University of Niš Niš Serbia Applied Engineering Sciences Academy of Vocational Studies Polytechnic Belgrade Serbia Faculty of Mathematics and Natural Sciences Cyril and Methodius University Skopje Republic of North Macedonia Informatics and Computer Science Technical School in Novi Pazar Novi Pazar Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

In this paper, a micro-MRC receiver with L branches operating in a correlated gamma-shadowed η-μ fading channel is considered. For such a system, the channel capacity (CC) is determined based on the maximum signal-to-noise ratio (SNR) at the L branches at the input of the MRC receiver. The results are presented graphically to illustrate the impact of different system parameters on performance and the improvements that result from the advantages of combined diversity. Additionally, we introduce the application of Large Language Models (LLMs) to make network experimentation more practical, using the previously presented expression as a case study. Moreover, a Vision Language Model (VLM) aided method aiming to make model-driven network design and experimentation more convenient is introduced and evaluated for the previously considered case study

关键词： Fading channels Wireless communication Visualization Channel capacity System performance Scalability Large language models Receivers Signal to noise ratio

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Persona-L has Entered the Chat: Leveraging LLMs and Ability-based Framework for Personas of People with Complex Needs 25

Persona-L has Entered the Chat: Leveraging LLMs and Ability-...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Lipeipei Sun Tianzi Qin Anran Hu Jiale Zhang Shuojia Lin Jianyan Chen Mona Ali Mirjana Prpa Khoury College of Computer Sciences Northeastern University Seattle Washington USA Khoury College of Computer Sciences Northeastern University Vancouver British Columbia Canada Faculty of Computer Science and Informatics Suez Canal University Ismailia Egypt Khoury College of Computer Sciences Northeastern University Vancouver British Columbia Canada and School of Interactive Arts+Technology Simon Fraser University Vancouver British Columbia Canada

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Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation

arXiv

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arXiv 2025年

作者： Thillainathan, Sarubi Yuan, Songchen Lee, En-Shiun Annie Jayasena, Sanath Ranathunga, Surangika Department of Computer Science and Engineering University of Moratuwa Sri Lanka Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Department of Computer Science University of Toronto Canada Faculty of Science University of Ontario of Technology Canada School of Mathematical and Computational Sciences Massey University New Zealand

Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage fine-tuning methods struggle in extremely low-resource NMT settings, where training data is very limited. This paper contributes to artificial intelligence by proposing two approaches for adapting msLLMs in these challenging scenarios: (1) continual pre-training (CPT), where the msLLM is further trained with domain-specific monolingual data to compensate for the under-representation of LRLs, and (2) intermediate task transfer learning (ITTL), a method that fine-tunes the msLLM with both in-domain and out-of-domain parallel data to enhance its translation capabilities across various domains and tasks. As an application in engineering, these methods are implemented in NMT systems for Sinhala, Tamil, and English (six language pairs) in domain-specific, extremely low-resource settings (datasets containing fewer than 100,000 samples). Our experiments reveal that these approaches enhance translation performance by an average of +1.47 bilingual evaluation understudy (BLEU) score compared to the standard single-stage fine-tuning baseline across all translation directions. Additionally, a multi-model ensemble further improves performance by an additional BLEU score. © 2025, CC BY.

关键词： Neural machine translation

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A Dynamic Optimization Framework for Computation Rate Maximization in UAV-Assisted Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Wang, Bi Qin, Shuo Wang, Yintong Xi'an University of Science and Technology Artificial Intelligence and Computer Science Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Jiangxi University of Science and Technology Faculty of Information Engineering Ganzhou China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Nanjing Xiaozhuang University School of Information Engineering Nanjing China

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms. © 1967-2012 IEEE.

关键词： Energy utilization

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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khan, Muhammad Amir Mazhar, Tehseen Ali, Muhammad Danish Khattak, Umar Farooq Shahzad, Tariq Saeed, Mamoon M. Hamam, Habib School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Malaysia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Abbottabad Campus Abbottabad 22060 Pakistan School of Information Technology UNITAR International University Kelana Jaya Petaling Jaya 47301 Malaysia Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Sana’a 00967 Yemen Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Hodmas University College Taleh Area Mogadishu Banadir 521376 Somalia Bridges for Academic Excellence—Spectrum Tunis Centre-Ville 1002 Tunisia Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The sys

关键词： Advanced adaptive fine-tuned Convolution neural networks Melanoma and non-melanoma skin cancer

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CNN to BiLSTM: Enhancing Setswana Named Entity Recognition 20th

CNN to BiLSTM: Enhancing Setswana Named Entity Recognition

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20th International Conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Chabalala, Shumile Owolawi, Pius Ojo, Sunday Department of Computer Systems Engineering Faculty of Information and Communication Technology Tshwane University of Technology Block K 2 Aubrey Matlakala St Soshanguve - K Soshanguve0152 South Africa Department of Information Technology Faculty of Accounting and Informatics Durban University of Technology Ritson Campus Durban4000 South Africa

ISBN: (纸本)9783031858550

In order to handle Named entity recognition (NER) in the Setswana language, this study applies transfer learning from convolutional neural networks (CNN) to bidirectional long short-term memory (BiLSTM) networks. We propose a transfer learning approach that leverages an existing CNN model trained to detect features in Setswana text data. Then, utilizing these attributes, a BiLSTM model specifically designed for NER is updated with the goal of improving performance and overcoming Setswana's lack of annotated data. Our study on Setswana NER corpora shows the effectiveness of this transfer learning method by outperforming conventional NER models. The new model has an improved F1-Score of 96% over the existing ones. This research contributes to the expansion of NER methods for languages with low resources. © The Author(s) 2025.

关键词： Convolutional neural networks

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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