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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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The exposome and pancreatic cancer, lifestyle and environmental risk factors for PDAC

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Seminars in Cancer Biology 2025年 113卷 100-129页

作者： Peduzzi, Giulia Archibugi, Livia Farinella, Riccardo de Leon Pisani, Ruggero Ponz Vodickova, Ludmila Vodicka, Pavel Kraja, Bledar Sainz, Juan Bars-Cortina, David Daniel, Neil Silvestri, Roberto Uysal-Onganer, Pinar Landi, Stefano Dulińska-Litewka, Joanna Comandatore, Annalisa Campa, Daniele Hughes, David J. Rizzato, Cosmeri Department of Biology University of Pisa Pisa Italy Pancreato-Biliary Endoscopy and Endosonography Division Pancreas Translational and Clinical Research Center IRCCS Ospedale San Raffaele Milan Italy Biomedical Center Martin Bioinformatic Center Comenius University in Bratislava Jessenius Faculty of Medicine in Martin Slovakia Faculty of Medicine and Biomedical Center in Pilsen Charles University Pilsen Czech Republic University Clinic of Gastrohepatology University Hospital Center Mother Teresa Tirana Albania Department of Biochemistry and Molecular Biology University of Granada Granada Spain Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP) Madrid 28029 Spain GENYO. Centre for Genomics and Oncological Research. Genomic Oncology department Granada Spain Instituto de Investigación Biosanitaria Ibs.Granada Granada Spain Institut Català d'Oncologia (ICO) IDIBELL Unit of Biomarkers and Susceptibility (UBS) Oncology Data Analytics Program (ODAP) Catalan Institute of Oncology (ICO) L′Hospitalet del Llobregat Barcelona Spain Institut Català d'Oncologia (ICO) IDIBELL ONCOBELL Program Bellvitge Biomedical Research Institute (IDIBELL) L′Hospitalet de Llobregat Barcelona Spain Molecular Epidemiology of Cancer Group UCD Conway Institute School of Biomolecular and Biomedical Science University College Dublin Dublin Ireland Cancer Mechanisms and Biomarkers Research Group School of Life Sciences University of Westminster London United Kingdom Chair of Medical Biochemistry Medical College Jagiellonian University Krakow Poland General Surgery Unit Department of Translational Research and New Technologies in Medicine and Surgery University of Pisa Italy

Pancreatic cancer (PC), particularly pancreatic ductal adenocarcinoma (PDAC), is a significant global health issue with high mortality rates. PDAC, though only 3 % of cancer diagnoses, causes 7 % of cancer deaths due to its severity and asymptomatic early stages. Risk factors include lifestyle choices, environmental exposures, and genetic predispositions. Conditions like new-onset type 2 diabetes and chronic pancreatitis also contribute significantly. Modifiable risk factors include smoking, alcohol consumption, non-alcoholic fatty pancreatic disease (NAFPD), and obesity. Smoking and heavy alcohol consumption increase PC risk, while NAFPD and obesity, particularly central adiposity, contribute through chronic inflammation and insulin resistance. Refined sugar and sugar-sweetened beverages (SSBs) are also linked to increased PC risk, especially among younger individuals. Hormonal treatments and medications like statins, aspirin, and metformin have mixed results on PC risk, with some showing protective effects. The gut microbiome influences PC through the gut-pancreas axis, with disruptions leading to inflammation and carcinogenesis. Exposure to toxic substances, including heavy metals and chemicals, is associated with increased PC risk. Glycome changes, such as abnormal glycosylation patterns, are significant in PDAC development and offer potential for early diagnosis. Interactions between environmental and genetic factors are crucial in PDAC susceptibility. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) linked to PDAC, but gene-environment interactions remain largely unexplored. Future research should focus on polygenic risk scores (PRS) and large-scale studies to better understand these interactions and their impact on PDAC risk. © 2025 The Authors

关键词： Exposome Pancreatic Cancer Risk factors

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Wind gust detection using physical sensors in quadcopters capstone project 2018

arXiv

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arXiv 2019年

作者： Gu, Suwen Lin, Menghao Computer Science Department Data Analytics Program Fordham University United States

We propose the use of basic inertial measurement units (IMU) which contain sensors such as accelerometers and gyroscopes already on-board drones to detect the speed and direction of wind gusts. The ability to quickly sense wind gusts has many applications, the most notable of which is in flight assistance of the drone, where it may adjust motor power parameter to compensate for such external factors or steer the drone toward a safer direction. To illustrate the feasibility of the approach, we conducted studies to assess how reliably wind speed and wind direction can be detected while a quad-copter drone is hovering and then in-motion, using off-the-shelf classifiers. Empirical results with real-life data, collected on a micro aerial vehicle (MAV) in a physical room with a consumer-grade fan, show that (1.1) wind speed can be detected with high accuracy after training, not only on the same drone, but also across different drones of the same class, while (1.2) wind direction can be detected with high accuracy after training on the same drone, but with limited generalizability to other drones. (1.3) We demonstrate how real-time detection of wind speed, using offline trained models, is feasible and can be done with high accuracy. (2.1) Finally, we find the reason behind the lower accuracy for wind direction detection during the analysis of drones in-motion. Copyright © 2019, The Authors. All rights reserved.

关键词： Wind

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The spatial zonation of the murine placental vasculature is specified by epigenetic mechanisms

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Developmental Cell 2025年第10期60卷 1467-1482.e8页

作者： Gehrs, Stephanie Jakab, Moritz Gutjahr, Ewgenija Gu, Zuguang Weichenhan, Dieter Mallm, Jan-Philipp Mogler, Carolin Schlesner, Matthias Plass, Christoph Schlereth, Katharina Augustin, Hellmut G. Division of Vascular Oncology and Metastasis German Cancer Research Center (DKFZ) Heidelberg 69120 Germany European Center for Angioscience (ECAS) Medical Faculty Mannheim Heidelberg University Mannheim 68167 Germany Faculty of Biosciences Heidelberg University Heidelberg 69120 Germany Institute of Pathology University Clinic Heidelberg Heidelberg 69120 Germany Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Cancer Epigenomics German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and Bioquant Heidelberg 69120 Germany Institute of Pathology TUM School of Medicine Technical University of Munich Munich 80333 Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg 86159 Germany

The labyrinthian fetoplacental capillary network is vital for proper nourishment of the developing embryo. Dysfunction of the maternal-fetal circulation is a primary cause of placental insufficiency. Here, we show that the spatial zonation of the murine placental labyrinth vasculature is controlled by flow-regulated epigenetic mechanisms. Spatiotemporal transcriptomic profiling identified a gradual change in the expression of epigenetic enzymes, including the de novo DNA methyltransferase 3a (DNMT3A). Loss of Dnmt3a resulted in DNA hypomethylation and perturbation of zonated placental gene expression. The resulting global DNA hypomethylation impaired the angiogenic capacity of endothelial cells. Global or endothelium-predominant deletion of Dnmt3a resulted in impaired placental vascularization and fetal growth retardation (FGR). Human placental endothelial gene expression profiling associated preeclampsia with reduced DNMT3A expression. Collectively, our study identified DMNT3A as critical methylome-regulator of placental endothelial gene expression and function with clinical implications for placental dysfunction, as it occurs during preeclampsia or FGR. © 2024 The Authors

关键词： angiogenesis DNA methylation DNMT3A epigenetic regulation growth retardation placental endothelium spatial zonation

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Effect of foliar nutrients application to the growth and yield of Physalis peruviana and Physalis alkekengi

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IOP Conference Series: Earth and Environmental science 2022年第1期978卷

作者： Diana Nurus Sholehah Dini Ermavitalini Miratul Khasanah Edy Setiti Wida Utami Hery Purnobasuki Doctoral Study Program of Mathematics and Natural Science Faculty of Science and Technology Airlangga University Indonesia Study Program of Agroecotechnology Faculty of Agriculture University of Trunojoyo Madura Indonesia Departement of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Indonesia Departement of Chemistry Faculty of Science and Technology Airlangga University Indonesia Departement of Biology Faculty of Science and Technology Airlangga University Indonesia

Physalis Sp. is a potential medicinal plant and functional food. The use of appropriate nutrients can gain optimal plant growth and productivity. This study aimed to determine the macro and micronutrients that can provide optimal growth and yield of Physalis sp. This research was conducted at a greenhouse in Bangkalan Madura. A factorial randomized block design with four replications was used in this study. The first factor was the species of Physalis Sp., namely P. peruviana and P. alkekengi. The second factor is nutrition at four levels: a spray of water only as control; foliar application of macronutrients at a dose of 2 g/L; foliar application of micronutrients at a dose of 0,125 g/L; foliar Application of a combination of macronutrients 1 g/L and micronutrients 0,0625 g/L. The data obtained were analyzed using analysis of variance (ANOVA) followed by Least Significant Difference (LSD) at the 5 % level. Nutrition significantly affected growth (plant height, stem diameter, and number of leaves) and yield (number of fruit). The interaction between treatments occurred in the number of fruits. Micronutrients gave the highest number of fruit, both species of Physalis sp.

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Machine learning applications for therapeutic tasks with genomics data

arXiv

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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Characterization and Classification of Purity of Limestone in Madura Island for Industrial Application

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Journal of Physics: Conference Series 2021年第1期1951卷

作者： F Munawaroh L K Muharrami Triwikantoro Z Arifin Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The characterization and classification of purity of limestone at Madura Island was investigated. Sampling was taken from nine quarries from different areas. The chemical analysis was carried out by X-ray fluorescence (XRF); the crystalline phase was characterized by X-ray diffraction (XRD). Analysis of purity classification based on XRF and XRD results according to British Geological Survey. Limestone has different purity, there are very high, medium, low and impure purity. The sample has a very high purity from Pamekasan (P.1) with CaO content of 99.06 wt% and 100% calcite. The samples are medium purity from Bangkalan (B.1, B.3), Pamekasan (P.2), Sumenep (S.1, and S.3) with CaO content of 91.61- 93.67 wt%. The samples are low purity from S.2 with CaO content 85.79 wt% and 100% dolomite. And the sample has impurities from P.3 and B.2 with CaO content 82.3 wt% and 84.7 wt%. Limestone with very high and medium purity which is processed into PCC can be applied in many industries as filler in rubber, paper making, plastic, paint, food, pharmaceutical, ceramic, adhesives, sealants, agriculture, and animal feed.

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The Role of Lactate Metabolism in Heart Failure and Cardiogenic Shock: Clinical Insights and Therapeutic Implications

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Journal of Cardiac Failure 2025年

作者： YUZEFPOLSKAYA, MELANA SCHWARTZ, SARAH LADANYI, ANNAMARIA ABRAHAM, JACOB GALE, CHRIS P. GRINSTEIN, JONATHAN KLEIN, LIVIU MAJURE, DAVID T. PHANCAO, ANITA SHEIKH, FAROOQ H. COLOMBO, PAOLO C. JANUZZI, JAMES L. MOLINA, EZEQUIEL J. Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Columbia University New York NY United States Center for Cardiovascular Analytics Research & Data Science (CARDS) Providence St. Joseph Research Network Portland OR United States Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Leeds Institute for Data Analytics University of Leeds Leeds UK Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Cardiovascular Medicine Department of Cardiology University of Chicago Chicago IL United States Division of Cardiovascular Medicine Department of Cardiology University of San Francisco San Francisco CA United States Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Weill Cornell Medical College New York NY United States Division of Cardiovascular Medicine Department of Cardiology University of Miami Miami FL United States Advanced Heart Failure Program MedStar Health Georgetown University School of Medicine Washington D.C. United States Cardiology Division Massachusetts General Hospital Harvard Medical School Baim Institute for Clinical Research Boston MA United States Division of Cardiothoracic Surgery Department of Surgery Piedmont Heart Institute Atlanta GA United States

Heart failure (HF) is associated with poor prognosis, especially when it progresses to cardiogenic shock (CS), where survival rates substantially decline. A key area of interest is the role of blood lactate as a biomarker in these conditions. Lactate is produced under normal physiological conditions but increases with impaired tissue perfusion, a hallmark of HF and CS. Elevated lactate levels result from increased production, reduced clearance or both and are often associated with worse outcomes. Traditionally considered a byproduct of anaerobic metabolism, lactate is now recognized as an important energy substrate, particularly in myocardial tissue during periods of metabolic stress. Recent studies suggest that dynamic lactate monitoring, including lactate clearance (LC), may provide critical insights into patients’ prognoses and responses to therapy. Serial measurements of lactate have been shown to predict survival in critically ill patients, including those with HF and CS. In CS, elevated lactate levels correlate with increased mortality risk, and LC is emerging as an important parameter in treatment protocols. Despite growing evidence of lactate's clinical relevance, research is needed to establish standardized thresholds and optimal monitoring timelines. Understanding the complexities of lactate metabolism and its role in HF and CS could lead to improved risk stratification and more personalized treatment approaches. © 2025

关键词： cardiogenic shock Heart failure lactate levels

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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