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Saadat H, Shah B, Halim Z, Anwar S. Knowledge Graph-Based Convolutional Network Coupled With Sentiment Analysis Towards Enhanced Drug Recommendation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:983-994. [PMID: 36441898 DOI: 10.1109/tcbb.2022.3225234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.
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Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
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Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
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Iqbal S, Ge F, Li F, Akutsu T, Zheng Y, Gasser RB, Yu DJ, Webb GI, Song J. PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations. J Chem Inf Model 2022; 62:4270-4282. [PMID: 35973091 DOI: 10.1021/acs.jcim.2c00799] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
An essential step in engineering proteins and understanding disease-causing missense mutations is to accurately model protein stability changes when such mutations occur. Here, we developed a new sequence-based predictor for the protein stability (PROST) change (Gibb's free energy change, ΔΔG) upon a single-point missense mutation. PROST extracts multiple descriptors from the most promising sequence-based predictors, such as BoostDDG, SAAFEC-SEQ, and DDGun. RPOST also extracts descriptors from iFeature and AlphaFold2. The extracted descriptors include sequence-based features, physicochemical properties, evolutionary information, evolutionary-based physicochemical properties, and predicted structural features. The PROST predictor is a weighted average ensemble model based on extreme gradient boosting (XGBoost) decision trees and an extra-trees regressor; PROST is trained on both direct and hypothetical reverse mutations using the S5294 (S2647 direct mutations + S2647 inverse mutations). The parameters for the PROST model are optimized using grid searching with 5-fold cross-validation, and feature importance analysis unveils the most relevant features. The performance of PROST is evaluated in a blinded manner, employing nine distinct data sets and existing state-of-the-art sequence-based and structure-based predictors. This method consistently performs well on frataxin, S217, S349, Ssym, S669, Myoglobin, and CAGI5 data sets in blind tests and similarly to the state-of-the-art predictors for p53 and S276 data sets. When the performance of PROST is compared with the latest predictors such as BoostDDG, SAAFEC-SEQ, ACDC-NN-seq, and DDGun, PROST dominates these predictors. A case study of mutation scanning of the frataxin protein for nine wild-type residues demonstrates the utility of PROST. Taken together, these findings indicate that PROST is a well-suited predictor when no protein structural information is available. The source code of PROST, data sets, examples, and pretrained models along with how to use PROST are available at https://github.com/ShahidIqb/PROST and https://prost.erc.monash.edu/seq.
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Affiliation(s)
- Shahid Iqbal
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria 3800, Australia.,Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Fang Ge
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Yuanting Zheng
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Geoffrey I Webb
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Jiangning Song
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria 3800, Australia.,Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Clayton, Victoria 3800, Australia
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Deshpande NM, Gite S, Aluvalu R. A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Comput Sci 2021; 7:e460. [PMID: 33981834 PMCID: PMC8080427 DOI: 10.7717/peerj-cs.460] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/06/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. METHODOLOGY A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. RESULTS Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. CONCLUSION There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.
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Affiliation(s)
- Nilkanth Mukund Deshpande
- Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India
- Electronics & Telecommunication Department, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, Ahmednagar, Maharashtra, India
| | - Shilpa Gite
- Department of Computer Science, Symbiosis Institute of Technology, Pune Symbiosis International (Deemed University), Pune, Maharashtra, India
- Symbiosis Center for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune, Maharashtra, India
| | - Rajanikanth Aluvalu
- Department of CSE, Vardhaman College of Engineering, Hyderabad, Telangana, India
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