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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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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

Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields

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arXiv 2024年

作者： Barton, Brandon Sagal, Jacob Feeney, Sean Grattan, George Patnaik, Pratik Oganesyan, Vadim Carr, Lincoln D. Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Physics and Astronomy College of Staten Island City University of New York Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center City University of New York New YorkNY10016 United States

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization. © 2024, CC BY.

关键词： Combinatorial optimization

Magnetized and quadratic convection based thermal transport in ternary radiative bio-nanofluid via intelligent neural networks: Two hidden layers mechanism

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Results in Physics 2024年 65卷

作者： Ayub, Assad Darvesh, Adil Shah, Syed Zahir Hussain Junjua, Moin-ud-Din Santos, Elizaldo Domingues dos Ahmad, Shabbir Hendy, Ahmed S. Ragab, Adham E. Department of Mathematics & Statistics Hazara University Manshera Pakistan School of Mathematical Sciences Zhejiang Normal University Zhejiang Jinhua 321004 China Programa de Pós-Graduação em Modelagem Computacional/LMCE Escola de Engenharia Universidade Federal do Rio Grande – FURG RS Rio Grande 96203900 Brazil FURG Escola de Engenharia RS Rio Grande Brazil Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan 60000 Pakistan Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg 620002 Russian Federation Department of Industrial Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Mathematics Ghazi University Dera Ghazi Khan 32200 Pakistan Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 96201-900 Rio Grande Brazil Department of Mechanics and Mathematics Western Caspian University Baku 1001 Azerbaijan

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al₂O₃, and TiO₂. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter. © 2024

关键词： Blood fluid Intelligent neural network Magnetized environment Ternary nanoparticles Thermal scrutiny

Expanders and right-angled Artin groups

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York United Kingdom New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Department of Mathematics University of Virginia CharlottesvilleVA22904 United States

The purpose of this article is to give a characterization of families of expander graphs via right-angled Artin groups. We prove that a sequence of simplicial graphs {Γi}i∈ forms a family of expander graphs if and only if a certain natural mini-max invariant arising from the cup product in the cohomology rings of the groups {A(Γi)}i∈ agrees with the Cheeger constant of the sequence of graphs, thus allowing us to characterize expander graphs via cohomology. This result is proved in the more general framework of vector space expanders, a novel structure consisting of sequences of vector spaces equipped with vector-space-valued bilinear pairings which satisfy a certain mini-max condition. These objects can be considered to be analogues of expander graphs in the realm of linear algebra, with a dictionary being given by the cup product in cohomology, and in this context represent a different approach to expanders that those developed by Lubotzky-Zelmanov and Bourgain-Yehudayoff. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts

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arXiv 2024年

作者： Ding, Jupiter Dalal, Roohi Sunayama, Tomomi Strauss, Michael A. Oguri, Masamune Okabe, Nobuhiro Hilton, Matt Monteiro-Oliveira, Rogério Sifón, Cristóbal Staggs, Suzanne T. Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States CIERA Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Political Science University of British Columbia VancouverBC Canada Department of Astronomy Steward Observatory University of Arizona TucsonAZ85721 United States Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Department of Physics Graduate School of Science Chiba University 1-33 Yayoi-Cho Inage-Ku Chiba263-8522 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi Hiroshima Campus Hiroshima739-8526 Japan Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Astrophysics Research Centre School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan Instituto de Física Pontificia Universidad Católica de Valparaíso Valparaíso Casilla4059 Chile Joseph Henry Laboratories of Physics Princeton University Jadwin Hall PrincetonNJ08544 United States

The "miscentering effect," i.e., the offset between a galaxy cluster’s optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich cluster catalog from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range 0.1 ≤ z ≤ 1.4 over an area of 469 deg2. By modeling the distribution of centering offsets in this fiducial sample, we find a miscentered fraction (corresponding to clusters offset by more than 330 kpc) of ∼ 25%, a value consistent with previous miscentering studies. We examine the image of each miscentered cluster in our sample and identify one of several reasons to explain the miscentering. Some clusters show significant miscentering for astrophysical reasons, i.e., ongoing cluster mergers. Others are miscentered due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentering from the sample, we find a considerably smaller miscentered fraction, ∼ 10%. We show that the gravitational lensing signal within 1 Mpc of miscentered clusters is considerably smaller than that of well-centered clusters, and we suggest that the ACT SZ centers are a better estimate of the true cluster potential centroid. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

Parameter Identifiability and Input-Output Equations

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arXiv 2020年

作者： Ovchinnikov, Alexey Pogudin, Gleb Thompson, Peter Department of Mathematics CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center NY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris France Department of Computer Science National Research University Higher School of Economics Moscow Russia Courant Institute of Mathematical Sciences New York University New York United States Ph.D. Program in Mathematics CUNY Graduate Center New York United States

Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. One of the standard approaches to assessing this problem is via input-output equations and, in particular, characteristic sets of differential ideals. The precise relation between identifiability and input-output identifiability is subtle. The goal of this note is to clarify this relation. The main results are: • identifiability implies input-output identifiability;• these notions coincide if the model does not have rational first integrals;• the field of input-output identifiable functions is generated by the coefficients of a "minimal" characteristic set of the corresponding differential ideal. We expect that some of these facts may be known to the experts in the area, but we are not aware of any articles in which these facts are stated precisely and rigorously proved. Copyright © 2020, The Authors. All rights reserved.

关键词： Rational functions

Skin cancer detection based on deep learning and entropy to detect outlier samples

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arXiv 2019年

作者： Pacheco, Andre G. C. Ali, Abder-Rahman Trappenberg, Thomas Graduate Program in Computer Science Federal University of Espírito Santo Faculty of Computer Science Dalhousie University Faculty of Natural Sciences Computing Science and Mathematics University of Stirling

We describe our methods to address both tasks of the ISIC 2019 challenge. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly. Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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ACS Nano 2025年第23期19卷 21538-21555页

作者： Cave, Joseph Christiono, Anne Schiavone, Carmine Pownall, Henry J. Cristini, Vittorio Staquicini, Daniela I. Pasqualini, Renata Arap, Wadih Brinker, C. Jeffrey Campen, Matthew Wang, Zhihui Van Nguyen, Hien Noureddine, Achraf Dogra, Prashant Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston 77030 TX United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York 10065 NY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy Department of Medicine Houston Methodist Houston 77030 TX United States Department of Medicine Weill Cornell Medicine New York 10065 NY United States Neal Cancer Center Houston Methodist Research Institute Houston 77030 TX United States Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston 77030 TX United States Rutgers Cancer Institute Newark 08901 NJ United States Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark 08901 NJ United States Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark 08901 NJ United States Department of Chemical and Biological Engineering University of New Mexico Albuquerque 87106 NM United States College of Pharmacy University of New Mexico Albuquerque 87106 NM United States Department of Physiology and Biophysics Weill Cornell Medical College New York 10065 NY United States Department of Electrical and Computer Engineering University of Houston Houston 77204 TX United States

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation. © 2025 The Authors. Published by American Chemical Society.

关键词： artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity PBPK

Optimization and Computing Model of Fish Resource Supply Chain Distribution Network

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Journal of Physics: Conference Series 2021年第1期1898卷

作者： Nurdin M Zarlis Tulus S Efendi Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia. Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia. Department of Mathematics Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sumatera Utara Medan Indonesia.

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers.

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