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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2

arXiv

引用

arXiv 2021年

作者： Bleher, Michael Hahn, Lukas Neumann, Maximilian Patiño-Galindo, Juan Ángel Carrière, Mathieu Bauer, Ulrich Rabadán, Raúl Ott, Andreas Mathematics Department Karlsruhe Institute of Technology Karlsruhe Germany Institute for Mathematics Heidelberg University Heidelberg Germany Program for Mathematical Genomics Department of Systems Biology Columbia University New YorkNY United States Department of Microbiology Icahn School of Medicine at Mount Sinai New YorkNY United States DataShape Centre Inria d’Université Côte d’Azur Biot France TUM Department of Mathematics Munich Data Science Institute Munich Germany

MSC Codes 62R40, 55N31, 68U05, 68T09, 92-08, 92C60, 92D15The COVID-19 pandemic has initiated an unprecedented worldwide effort to characterize its evolution through the mapping of mutations of the coronavirus SARS-CoV-2. The early identification of mutations that could confer adaptive advantages to the virus, such as higher infectivity or immune evasion, is of paramount importance. However, the large number of currently available genomes precludes the efficient use of phylogeny-based methods. Here we present CoVtRec, a fast and scalable Topological data Analysis approach for the surveillance of emerging adaptive mutations in large genomic datasets. Our method overcomes limitations of state-of-the-art phylogeny-based approaches by quantifying the potential adaptiveness of mutations merely by their topological footprint in the genome alignment, without resorting to the reconstruction of a single optimal phylogenetic tree. Analyzing millions of SARS-CoV-2 genomes from GISAID, we find a correlation between topological signals and adaptation to the human host. By leveraging the stratification by time in sequence data, our method enables the high-resolution longitudinal analysis of topological signals of adaptation. We characterize the convergent evolution of the coronavirus throughout the whole pandemic to date, report on emerging potentially adaptive mutations, and pinpoint mutations in Variants of Concern that are likely associated with positive selection. Our approach can improve the surveillance of mutations of concern and guide experimental studies. © 2021, CC BY-NC-ND.

关键词： Coronavirus

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

引用

Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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Temporal motifs in patent opposition and collaboration networks

arXiv

引用

arXiv 2021年

作者： Liu, Penghang Masuda, Naoki Kito, Tomomi Sarıyüce, Ahmet Erdem Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Tokyo Japan

Patents are intellectual properties that reflect innovative activities of companies and organizations. The literature is rich with the studies that analyze the citations among the patents and the collaboration relations among companies that own the patents. However, the adversarial relations between the patent owners are not as well investigated. One proxy to model such relations is the patent opposition, which is a legal activity in which a company challenges the validity of a patent. Characterizing the patent oppositions, collaborations, and the interplay between them can help better understand the companies’ business strategies. Temporality matters in this context as the order and frequency of oppositions and collaborations characterize their interplay. In this study, we construct a two-layer temporal network to model the patent oppositions and collaborations among the companies. We utilize temporal motifs to analyze the oppositions and collaborations from structural and temporal perspectives. We first characterize the frequent motifs in patent oppositions and investigate how often the companies of different sizes attack other companies. We show that large companies tend to engage in opposition with multiple companies. Then we analyze the temporal interplay between collaborations and oppositions. We find that two adversarial companies are more likely to collaborate in the future than two collaborating companies oppose each other in the future. © 2021, CC BY.

关键词： Patents and inventions

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Antioxidant and Antidiabetic Activities of Melastoma Malabathricum Leaves Extracts

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Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2022年第7期49卷 144-153页

作者： Idris, Muddatstsir Purnomo, Adi Setyo Martak, Fahimah Fatmawati, Sri Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia Program of Pharmacy Study Faculty of Mathematics and Natural Sciences University of Lambung Mangkurat A. Yani KM. 35.8 Banjarbaru South Kalimantan70714 Indonesia Agri-Food and Biotechnology Research Center Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia

Currently, no effective medication is available to treat diabetes despite this disease is a serious health problem. As part of our project to explore Indonesian medicinal plants for antidiabetic agents, this study aimed to investigate the total phenolic and flavonoid contents, antioxidant and antidiabetic properties of Melastoma malabathricum leaves extracts. Spectrophotometric methods were used to determine the total phenolic and flavonoid contents. Antioxidant activity was performed using DPPH, ABTS, and FRAP methods. The in vitro antidiabetic test was conducted through an inhibitory evaluation of α-glucosidase and α-amylase, while STZ-induced diabetic rats were used for in vivo study. The highest value of total phenolic (183.71 ± 0.11 mg GAE/g Extract) was recorded in the methanolic extract and flavonoid (24.10 ± 0.04 mg QE/g Extract) contents were recorded in the EtOAc extract. The methanolic extract has the highest DPPH and ABTS activities with IC50 values of 8.58 ± 0.03 and 4.59 ± 0.03 μg/mL, respectively. It also showed the highest FRAP activity with a 51.15 ± 0.10 µM Fe2+/g. In vitro antidiabetic testing of the methanolic extract of leaves against α-glucosidase and α-amylase was reported for the first time. This novel result showed that the methanolic extract inhibited α-glucosidase and α-amylase with IC50 values of 75.25±1.60 and 52.38±1.32 μg/mL, respectively. A dose of 200 mg/Kg body weight of the methanolic extract reduced rats’ blood glucose rate and serum blood glucose by 51.01% and 37.82%, respectively, after 15 days of treatments. These findings suggested that the methanolic extract of M. malabathricum leaves can be used as a potential source of antioxidant and antidiabetic agents. © 2022 Hunan University. All rights reserved.

关键词： Antioxidants

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Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study

引用

Journal of medical Internet research 2023年 25卷 e48834页

作者： Chen, Hung-Hsun Lu, Henry Horng-Shing Weng, Wei-Hung Lin, Yu-Hsuan Department of Mathematics Fu Jen Catholic University New Taipei City Taiwan Program of Artificial Intelligence & Information Security Fu-Jen Catholic University New Taipei City Taiwan Institute of Statistics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Statistics and Data Science Cornell University Ithaca NY United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MN United States Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Psychiatry National Taiwan University Hospital Taipei Taiwan Department of Psychiatry College of Medicine National Taiwan University Taipei Taiwan

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE: In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS: To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day;P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS: Our

关键词： deep learning digital phenotyping extreme gradient-boosted trees human-smartphone interaction machine learning one-dimensional convolutional neural network probability in work mode work hours

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Solution of SEIRD mathematical model for the COVID-19 transmission using microsoft excel

引用

AIP Conference Proceedings 2023年第1期2594卷

作者： D. P. W. Putra M. A. Rudhito D. Sudigyo T. Suparyanto B. Pardamean Department of Mathematics Education Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Mathematical modeling in the epidemiology study can be applied to describe the current transmission of viruses, one of which is compartments. However, this simulation model is rig...

Mathematical modeling in the epidemiology study can be applied to describe the current transmission of viruses, one of which is compartments. However, this simulation model is rigorous to understand, especially in interpreting the parameter values in influencing the solution. Therefore, it is necessary to present a coherent mathematical model solution. This study aims to determine the SEIRD model solution in the COVID-19 transmission using Microsoft Excel with three conditions (normal, new normal, and lockdown) to facilitate the interpretation of data. The SEIRD model used in this study considers natural population growth, namely natural births and deaths. Three stages to evaluate the model solution in this study are constructing a mathematical model, deciding the parameter intervals, and creating an applet in Microsoft Excel. The system of differential equations is converted into a system of difference equations to obtain numerical model solutions. The results showed that the differences in the infection rates for old normal, new normal, and lockdown conditions were 24%, 4%, and 3%, respectively.

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The development of video analysis instrument to determine teacher's character

引用

AIP Conference Proceedings 2023年第1期2594卷

作者： Y. B. Gromang M. A. Rudhito D. Sudigyo T. Suparyanto B. Pardamean Department of Mathematics Education Universitas Sanata Dharma Paingan Maguwoharjo Depok Sleman Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This study aims to develop a valid and reliable video analysis instrument to determine teachers’ character in the opening lesson in terms of its utterances and produce video analysis on micro-teaching that will be used in training related to Artificial Intelligence (AI). The type of research used is research and development. The subject of this research is the micro-teaching video of class E students’ batch 2019, mathematics Education Study program at Sanata Dharma University. The product of this research is a video analysis instrument to find out the character of the prospective teachers, such as confidence, enthusiasm, and happiness in terms of voice utterances. This study uses the ADDIE research model. The results show that the video analysis instrument has a validity value of 4.80 which means it's very valid. Video analysis instrument to find out the enthusiastic character has a value of validity of 4.70, which means it's very valid. Then, the video analysis instrument to find out the character of happiness has a validity value of 4.60 which means it's very valid. The practicality of the developed video analysis instrument meets the required criteria with a practicality value of 4.31. Overall, the video analysis instrument developed belongs to an effective category.

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A Consensus Privacy Metrics Framework for Synthetic data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Study on structure and morphology of silicon-substituted LiFePO4/C doped material

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AIP Conference Proceedings 2023年第1期2604卷

作者： Fahmi Astuti Vera L. Maghfirohtuzzoimah Sahara H. Intifadhah Pelangi Az-Zahra Muhammad R. Ramadhan Mochamad Zainuri Darminto Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Study Program of Physics Faculty of Mathematics and Natural Sciences Republic of Indonesia Defense University Bogor Indonesia

Elemental doping and carbon coating on the electrode are able to increase the battery’s performance to be a good energy storage. In this case, some studies are necessary to confirm the quality of the cathode material, LiFePO4 on the Li-ion based battery material (LIB). Here we report the study of structure and morphology of LiFePO4 by silicon doping at the P-atom atomic position and carbon coating (henceforth called as LFP-Si) utilizing the X-Ray diffractometer, the Fourier-transform infrared technique, and both the scanning and transmission electron microscopy (SEM & TEM), as a preliminary investigation to know the structure and morphology of LiFePO4 after elemental doping and carbon coating. All characterization results are essential for the improvement of the performance of LiFePO4/C doped material.

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