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Razzak MA, Islam MN, Aadeeb MS, Tasnim T. Digital health interventions for cervical cancer care: A systematic review and future research opportunities. PLoS One 2023; 18:e0296015. [PMID: 38100494 PMCID: PMC10723694 DOI: 10.1371/journal.pone.0296015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations. METHODS This study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion. RESULTS The review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care. CONCLUSIONS The findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society.
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Affiliation(s)
- Md Abdur Razzak
- Faculty of Science and Technology, Bangladesh University of Professionals, Dhaka, Bangladesh
| | - Muhammad Nazrul Islam
- Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh
| | - Md Shadman Aadeeb
- Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh
| | - Tasfia Tasnim
- Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh
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2
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Shim E, Tewari A, Cernak T, Zimmerman PM. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J Chem Inf Model 2023; 63:3659-3668. [PMID: 37312524 PMCID: PMC11163943 DOI: 10.1021/acs.jcim.3c00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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3
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Manzhos S, Tsuda S, Ihara M. Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality. Phys Chem Chem Phys 2023; 25:1546-1555. [PMID: 36562317 DOI: 10.1039/d2cp04155c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) based methods and tools have now firmly established themselves in physical chemistry and in particular in theoretical and computational chemistry and in materials chemistry. The generality of popular ML techniques such as neural networks or kernel methods (Gaussian process and kernel ridge regression and their flavors) permitted their application to diverse problems from prediction of properties of functional materials (catalysts, solid state ionic conductors, etc.) from descriptors to the building of interatomic potentials (where ML is currently routinely used in applications) and electron density functionals. These ML techniques are assumed to have superior expressive power of nonlinear methods, and are often used "as is", with concepts such as "non-parametric" or "deep learning" used without a clear justification for their need or advantage over simpler and more robust alternatives. In this Perspective, we highlight some interrelations between popular ML techniques and traditional linear regressions and basis expansions and demonstrate that in certain regimes (such as a very high dimensionality) these approximations might collapse. We also discuss ways to recover the expressive power of a nonlinear approach and to help select hyperparameters with the help of high-dimensional model representation and to obtain elements of insight while preserving the generality of the method.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
| | - Shunsaku Tsuda
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
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4
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Su A, Zhang X, Zhang C, Ding D, Yang YF, Wang K, She YB. Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts. Phys Chem Chem Phys 2023; 25:10536-10549. [PMID: 36987933 DOI: 10.1039/d3cp00917c] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
A deep transfer learning approach is used to predict HOMO/LUMO energies of organic materials with a small amount of training data.
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Affiliation(s)
- An Su
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Xin Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Chengwei Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Debo Ding
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yun-Fang Yang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Keke Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yuan-Bin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
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5
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del Cueto M, Rawski-Furman C, Aragó J, Ortí E, Troisi A. Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2022; 126:13053-13061. [PMID: 35983311 PMCID: PMC9376947 DOI: 10.1021/acs.jpcc.2c04725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/15/2022] [Indexed: 06/15/2023]
Abstract
We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset. We discuss the effect of data biases on the distribution of perovskite families/architectures on the model's accuracy and offer an analysis with a subset of the data to accurately study the effect of the hole-transporting material on the solar cell performance. Finally, we discuss some chemical fragments, like arylamine and aryloxy groups, which present a relatively large positive correlation with the efficiency of the cell, whereas other groups, like thiophene groups, display a negative correlation with power conversion efficiency (PCE).
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Affiliation(s)
- Marcos del Cueto
- Department
of Chemistry, University of Liverpool, Liverpool L69 3BX, U.K.
| | | | - Juan Aragó
- Instituto
de Ciencia Molecular (ICMol), Universidad
de Valencia, Catedrático José Beltrán 2, Paterna 46980, Spain
| | - Enrique Ortí
- Instituto
de Ciencia Molecular (ICMol), Universidad
de Valencia, Catedrático José Beltrán 2, Paterna 46980, Spain
| | - Alessandro Troisi
- Department
of Chemistry, University of Liverpool, Liverpool L69 3BX, U.K.
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6
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Zhao ZW, Del Cueto M, Troisi A. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors. DIGITAL DISCOVERY 2022; 1:266-276. [PMID: 35769202 PMCID: PMC9189862 DOI: 10.1039/d2dd00004k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/23/2022] [Indexed: 11/21/2022]
Abstract
We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation. We consider as a case study the problem of the discovery of non-fullerene acceptors because novel classes of acceptors are naturally classified into distinct chemical families. We show that conventional ML methods are not useful in practice when attempting to predict the efficiency of a completely novel class of materials. The approach proposed in this work increases the accuracy of the predictions to enable at least the categorization of materials with a performance above and below the median value.
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Affiliation(s)
- Zhi-Wen Zhao
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
- Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University Changchun 130024 Jilin P. R. China
| | - Marcos Del Cueto
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
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7
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Liu W, Wu Y, Hong Y, Zhang Z, Yue Y, Zhang J. Applications of machine learning in computational nanotechnology. NANOTECHNOLOGY 2022; 33:162501. [PMID: 34965514 DOI: 10.1088/1361-6528/ac46d7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.
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Affiliation(s)
- Wenxiang Liu
- Key Laboratory of Hydraulic Machinery Transients (MOE), School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, People's Republic of China
| | - Yongqiang Wu
- Weichai Power CO., Ltd, Weifang 261061, People's Republic of China
| | - Yang Hong
- Research Computing, RCAC, Purdue University, West Lafayette, IN 47907, United States of America
| | - Zhongtao Zhang
- Holland Computing Center, University of Nebraska-Lincoln, Lincoln, NE, United States of America
| | - Yanan Yue
- Key Laboratory of Hydraulic Machinery Transients (MOE), School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, People's Republic of China
| | - Jingchao Zhang
- NVIDIA AI Technology Center (NVAITC), Santa Clara, CA 95051, United States of America
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8
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Xu S, Li J, Cai P, Liu X, Liu B, Wang X. Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations. J Am Chem Soc 2021; 143:19769-19777. [PMID: 34788033 DOI: 10.1021/jacs.1c08211] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet-triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.
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Affiliation(s)
- Shidang Xu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Jiali Li
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Pengfei Cai
- Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
| | - Xiaoli Liu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Bin Liu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore.,Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, China
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
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9
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Omar ÖH, Del Cueto M, Nematiaram T, Troisi A. High-throughput virtual screening for organic electronics: a comparative study of alternative strategies. JOURNAL OF MATERIALS CHEMISTRY. C 2021; 9:13557-13583. [PMID: 34745630 PMCID: PMC8515942 DOI: 10.1039/d1tc03256a] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/13/2021] [Indexed: 06/01/2023]
Abstract
We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.
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Affiliation(s)
- Ömer H Omar
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Marcos Del Cueto
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | | | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
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