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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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Ripeness stage characterization of coffee fruits ( coffea arabica L. var. Castillo ) applying chromaticity maps obtained from digital images

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Materials Today: Proceedings 2021年 44卷 1271-1278页

作者： Alexander Rincon-Jimenez Hector A. Tinoco Jaime Buitrago-Osorio Olga Ocampo Lina V. Berrio Jose L. Rodriguez-Sotelo Carlos Arizmendi Experimental and Computational Mechanics Laboratory Universidad Autónoma de Manizales Edificio Fundadores C.P. 170001 Manizales-Caldas Colombia Doctoral Program Universidad Autónoma de Manizales Edificio Fundadores C.P. 170001 Manizales-Caldas Colombia Institute of Physics of Materials Academy of Sciences of the Czech Republic Žižkova 22 616 62 Brno Czech Republic Central European Institute of Technology (CEITEC) Brno University of Technology Purkyňova 123 612 00 Brno Czech Republic Departament of Economy and Administration Universidad Autónoma de Manizales. Edificio Fundadores C.P. 170001 Manizales-Caldas Colombia Mechatronics Engineering Program Universidad Autonoma de Bucaramanga Bucaramanga-Santander Colombia

The harvesting is one of the most expensive stages during Colombian coffee production since the selectivity of ripe fruits (from the trees) is carried manually nowadays. However, the scarcity of workforce has become a new challenge for developing technologies that can be used in situations that traditional mechanized harvesters cannot be implemented. The classification of coffee fruits through the ripeness could help create selective harvesting methods from the different engineering fields (robotic, artificial vision, vibratory systems, etc.). This study describes a methodology to characterize the coffee fruit ripeness (Coffea arabica var. Castillo) using color maps obtained from digital image processing and colorimetry. Ninety-two samples were measured, and for each sample, a digital image was processed to extract the most representative color with an algorithm (RGB space), and parallel to this, the chromaticity was measured with a colorimeter (CIELab space). Both color spaces were related through linear transformations computed from the data. Results show that the ripeness classification is well defined in both color spaces; however, RGB space was divided into four ripening since the ripeness timeline was linearly represented. Through the transformations, the image data were contrasted with those measured with the colorimeter, which demonstrated that it could be converted in a colorimeter under certain specific conditions.

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Lattice Misorientation Evolution and Grain Refinement in Al-Si Alloys under High-Strain Shear Deformation

SSRN

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SSRN 2021年

作者： Gwalani, Bharat Fu, Wenkai Olszta, Mathew Silverstein, Joshua Yadav, Digvijay R. Manimunda, Praveena Guzman, Anthony Xie, Kelvin Rohatgi, Aashish Mathaudhu, Suveen Powell, Cynthia A. Sushko, Peter V. Li, Yulan Devaraj, Arun Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Energy and Environment Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Applied Process Engineering Laboratory Pacific Northwest National Laboratory RichlandWA99354 United States Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States Bruker Nano Surfaces Division 9625 West 76th Street Eden PrairieMN55344 United States Material Science and Engineering Program University of California Riverside RiversideCA United States

The starting alloy microstructure can be tailored to achieve varying degrees of grain refinement and enhance mechanical properties through severe plastic shear deformation during solid-phase processing. Crystal plasticity-based grain misorientation modeling, coupled with systematic pin-on-disk tribometry-based subsurface shear deformation experiments on as-cast Al-xSi alloys (x = 0, 1, 4 at %), was conducted. The post-deformation microstructural analysis, through a combined computational and experimental approach, conclusively shows that the initial volume fraction of the hard Si phase enhances the evolution of local lattice misorientation, leading to efficient grain refinement during severe plastic shear deformation. The shear-deformation–induced nanostructure resulted in more than double the nanoindentation hardness in the processed alloy. © 2021, The Authors. All rights reserved.

关键词： Aluminum alloys

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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling

Npj imaging

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Npj imaging 2025年第1期3卷 5页

作者： Ayush Saurabh Peter T Brown J Shepard Bryan Iv Zachary R Fox Rory Kruithoff Cristopher Thompson Comert Kural Douglas P Shepherd Steve Pressé Center for Biological Physics Arizona State University Tempe AZ USA. Department of Physics Arizona State University Tempe AZ USA. Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA. Department of Physics The Ohio State University Columbus OH USA. Interdisciplinary Biophysics Graduate Program The Ohio State University Columbus OH USA. School of Molecular Sciences Arizona State University Tempe AZ USA.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

关键词： Biophysics Image processing Imaging and sensing Super-resolution microscopy

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Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Forecasting Vegetation Health in the MENA Region by Predicti...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Wenzhao Li Erik Linstead Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R 2 > 0.95) in the testing and (R 2 > 0.93) in the evaluation process.

关键词： Vegetation mapping MODIS Atmospheric modeling Remote sensing Predictive models Training Support vector machines

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The doctor will polygraph you now

Npj health systems

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Npj health systems 2024年第1期1卷 1页

作者： James Anibal Jasmine Gunkel Shaheen Awan Hannah Huth Hang Nguyen Tram Le Jean-Christophe Bélisle-Pipon Micah Boyer Lindsey Hazen Yael Bensoussan David Clifton Bradford Wood Center for Interventional Oncology Clinical Center National Institutes of Health (NIH) Bethesda MD USA. Computational Health Informatics Lab Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK. Department of Bioethics National Institutes of Health (NIH) Bethesda MD USA. Department of Communication Sciences & Disorders University of Central Florida Orlando FL USA. Global Infectious Disease Program Georgetown University Washington DC USA. College of Engineering University of South Florida Tampa FL USA. Faculty of Health Sciences Simon Fraser University Burnaby BC Canada. USF Health Voice Center Department of Otolaryngology-Head & Neck Surgery University of South Florida Tampa FL USA.

Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors that could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking", particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., "AI self-trust"). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.

关键词： Ethics Machine learning Science, technology and society

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Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

arXiv

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arXiv 2023年

作者： Lorenzo, Guillermo Ahmed, Syed Rakin Hormuth, David A. Vaughn, Brenna Kalpathy-Cramer, Jayashree Solorio, Luis Yankeelov, Thomas E. Gomez, Hector Department of Civil Engineering and Architecture University of Pavia Pavia27100 Italy Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital BostonMA02129 United States Harvard Graduate Program in Biophysics Harvard Medical School Harvard University CambridgeMA02115 United States Massachusetts Institute of Technology CambridgeMA02139 United States Geisel School of Medicine at Dartmouth Dartmouth College HanoverNH02139 United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX78712 United States Weldon School of Biomedical Engineering Purdue University West LafayetteIN47907 United States Department of Ophthalmology University of Colorado Anschutz DenverCO80045 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Department of Oncology The University of Texas at Austin AustinTX78712 United States Department of Imaging Physics MD Anderson Cancer Center HoustonTX77030 United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX78712 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Purdue Center for Cancer Research Purdue University West LafayetteIN47907 United States

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on "big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models. © 2023, CC BY-NC-SA.

关键词： Big data

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BPS2025 - Development and benchmarking of the open force field initiative self-consistent force field for proteins and small molecules

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Biophysical Journal 2025年第3期124卷 483a-484a页

作者： Chapin E. Cavender Pavan K. Behara Anika J. Friedman Joshua T. Horton Alexandra R. McIsaac Louis G. Smith Matthew W. Thompson Jeffrey Wagner Lily Wang Brent R. Westbrook John D. Chodera Daniel Cole David L. Mobley Michael R. Shirts Michael K. Gilson Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA Center for Neurotherapeutics University of California Irvine Irvine CA USA Department of Chemical and Biological Engineering University of Colorado Boulder Boulder CO USA School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne United Kingdom Open Force Field Consortium Open Molecular Software Foundation Davis CA USA Department of Biochemistry and Biophysics University of Pennsylvania Philadelphia PA USA Computational & Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Pharmaceutical Sciences and Department of Chemistry University of California Irvine Irvine CA USA

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computational Identification and Experimental Demonstration of High-Performance Methane Sorbents

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Angewandte Chemie 2022年第25期134卷

作者： Dr. Karabi Nath Dr. Alauddin Ahmed Prof. Dr. Donald J. Siegel Prof. Dr. Adam J. Matzger Department of Chemistry and Macromolecular Science and Engineering Program University of Michigan 930 North University Avenue Ann Arbor MI 48109 USA Mechanical Engineering Department University of Michigan Ann Arbor MI 48109 USA Materials Science and Engineering Applied Physics Program and University of Michigan Energy Institute University of Michigan Ann Arbor MI 48109 USA Current address: Walker Department of Mechanical Engineering Texas Materials Institute and Oden Institute for Computational Engineering and Sciences University of Texas at Austin 204 E. Dean Keeton Street ETC II 5.160 Austin TX 78712-1591 USA

Remarkable methane uptake is demonstrated experimentally in three metal-organic frameworks (MOFs) identified by computational screening: UTSA-76, UMCM-152 and DUT-23-Cu. These MOFs outperform the benchmark sorbent, HKUST-1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high-affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.

关键词： computational Screening Deliverable Capacity Interatomic Potentials Metal Organic Framework (MOF) Methane Storage

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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