Article
Computer Science, Artificial Intelligence
Arijit De, Ananda S. Chowdhury
Summary: This study utilizes 3D DTI for automated classification of Alzheimer's disease, involving parameters such as FA, MD, and EPI, using CNN and RFC models for training, and achieving a high classification accuracy through result fusion.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Agronomy
Yue Yu, Haiye Yu, Xiaokai Li, Lei Zhang, Yuanyuan Sui
Summary: This article proposes a method for predicting rice leaf potassium content (LKC) using spectral characteristics and random forests. The results show that the mixed variable obtained with the random forest feature selection method effectively improves the prediction accuracy of rice LKC. The regression models based on single band variables and vegetation index variables are the best models, and certain spectral ranges and vegetation indices are significant for predicting rice LKC.
Article
Computer Science, Artificial Intelligence
Yuang Shi, Chen Zu, Mei Hong, Luping Zhou, Lei Wang, Xi Wu, Jiliu Zhou, Daoqiang Zhang, Yan Wang
Summary: Multimodal classification methods using different modalities have advantages over traditional single-modality-based ones for the diagnosis of Alzheimer's disease and mild cognitive impairment. This paper proposes a novel multimodal feature selection method called ASMFS, which performs adaptive similarity learning and feature selection simultaneously, and demonstrates its effectiveness and superiority over other state-of-the-art approaches for multi-modality classification of AD/MCI.
PATTERN RECOGNITION
(2022)
Article
Chemistry, Multidisciplinary
Omer Asghar Dara, Jose Manuel Lopez-Guede, Hasan Issa Raheem, Javad Rahebi, Ekaitz Zulueta, Unai Fernandez-Gamiz
Summary: Alzheimer's is a neurodegenerative disorder that impairs detailed mental analysis and cognition, leading to mental decline. Recent advances in research have focused on identifying and addressing the progression of Alzheimer's disease. Genetic factors, stress, and nutrition have been found to play significant roles in the development of this condition.
APPLIED SCIENCES-BASEL
(2023)
Article
Biology
Ela Kaplan, Sengul Dogan, Turker Tuncer, Mehmet Baygin, Erman Altunisik
Summary: In this study, a new automatic AD detection model called LPQNet was proposed, demonstrating high classification accuracy on three different image datasets and showing superiority over other detection models. Additionally, LPQNet can be used to develop a new generation intelligent AD detection application for MRI and CT devices.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Public, Environmental & Occupational Health
C. Kavitha, Vinodhini Mani, S. R. Srividhya, Osamah Ibrahim Khalaf, Carlos Andres Tavera Romero
Summary: Alzheimer's disease is the leading cause of dementia in older adults, and there is interest in applying machine learning to discover metabolic diseases that affect a large population. With the increasing aging population, the importance of predicting and diagnosing Alzheimer's disease is becoming more significant.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Geriatrics & Gerontology
Feng Gu, Songhua Ma, Xiude Wang, Jian Zhao, Ying Yu, Xinjian Song
Summary: This study introduces a frequency-based criterion to evaluate the stability of feature selection and designs a pipeline to consider both stability and discriminability when selecting features. Experimental results on the ADNI dataset demonstrate the feasibility of this method.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Zeinab A. Dastgheib, Brian J. Lithgow, Zahra K. Moussavi
Summary: An automated feature extraction and selection algorithm is developed to accurately identify Alzheimer's disease (AD) and AD with cerebrovascular disease pathology (AD-CVD) from healthy controls using electrovestibulography (EVestG) signals. The algorithm achieved high classification accuracies and the results were consistent with previous studies.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Biochemistry & Molecular Biology
Yi-Long Huang, Chao-Hsiung Lin, Tsung-Hsien Tsai, Chen-Hua Huang, Jie-Ling Li, Liang-Kung Chen, Chun-Hsien Li, Ting-Fen Tsai, Pei-Ning Wang
Summary: This study utilized untargeted metabolomics analysis to characterize plasma metabolites in MCI patients progressing to AD and stable MCI patients. A 20-metabolite signature panel associated with the presence and progression of AD was identified using machine learning, offering predictive value for conversion from MCI to AD. Additionally, the bacteria-generated metabolite indole-3-propionic acid was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biotechnology & Applied Microbiology
Abhibhav Sharma, Pinki Dey
Summary: A machine learning approach was used to explore genetic risk factors of Alzheimer's disease, revealing novel and highly predictive biomarkers.
Article
Biochemistry & Molecular Biology
Leqi Tian, Wenbin Wu, Tianwei Yu
Summary: Random Forest (RF) is a popular machine learning method for classification and regression tasks, and it performs well under low sample size situations. However, there are issues with gene selection using RF as the important genes are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency. To address this issue, we propose the Graph Random Forest (GRF) method, which incorporates external topological information to identify highly connected important features. The algorithm achieves equivalent classification accuracy to RF while selecting interpretable feature sub-graphs.
Article
Computer Science, Information Systems
Cun Ji, Mingsen Du, Yanxuan Wei, Yupeng Hu, Shijun Liu, Li Pan, Xiangwei Zheng
Summary: Time series classification is widely used in various domains, including EEG/ECG classification, device anomaly detection, and speaker authentication. Despite the existence of many methods, selecting intuitive temporal features for accurate classification remains a challenge. Therefore, this paper proposes a new method called TSC-RTF, which utilizes random temporal features, and shows that it can compete with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Biochemical Research Methods
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty, Arcot Sowmya
Summary: Feature selection is commonly used to identify important features in a dataset but can be unstable in high-dimensional data. Ensemble feature selection with data-driven thresholds improves stability and produces more reproducible selections of features. This study applies data-driven thresholds to ensemble feature selectors in Alzheimer's disease studies, resulting in more stable results and reflecting current findings in the literature. Data-driven thresholds eliminate the need for a fixed threshold and select a more meaningful set of features, improving interpretability of disease models.
BMC BIOINFORMATICS
(2023)
Article
Automation & Control Systems
Jianyuan Sun, Hui Yu, Guoqiang Zhong, Junyu Dong, Shu Zhang, Hongchuan Yu
Summary: In this article, a new random forests algorithm called random Shapley forests (RSFs) is proposed, which uses the Shapley value to evaluate the importance of each feature. The experiments conducted on benchmark and real-world datasets demonstrate that RSFs outperform or are at least comparable to existing consistent RFs, original RFs, and support vector machines.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Heng Tao Shen, Xiaofeng Zhu, Zheng Zhang, Shui-Hua Wang, Yi Chen, Xing Xu, Jie Shao
Summary: A novel sparse regression method is proposed in this paper to fuse auxiliary data into predictor data for pMCI/sMCI classification in AD research. This method addresses the challenge of identifying differences between pMCI and sMCI subjects, while also handling outliers and age effects in the data.
INFORMATION FUSION
(2021)
Article
Chemistry, Medicinal
Maria Vittoria Togo, Fabrizio Mastrolorito, Fulvio Ciriaco, Daniela Trisciuzzi, Anna Rita Tondo, Nicola Gambacorta, Loredana Bellantuono, Alfonso Monaco, Francesco Leonetti, Roberto Bellotti, Cosimo Damiano Altomare, Nicola Amoroso, Orazio Nicolotti
Summary: This article presents a robust and reproducible eXplainable Artificial Intelligence (XAI) approach for predicting developmental toxicity, which is a challenging human-health endpoint in toxicology. The proposed framework compares favorably with state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, providing a reliable support system for ensuring informativeness, uncertainty estimation, generalization, and transparency in developmental toxicity. The model utilizes specific molecular descriptors and structural alerts to distinguish toxic and nontoxic chemicals.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Environmental Sciences
Roberto Cazzolla Gatti, Arianna Di Paola, Alfonso Monaco, Alena Velichevskaya, Nicola Amoroso, Roberto Bellotti
Summary: Tumours have become the second leading cause of death after cardiovascular diseases. Recent research suggests that environmental pollution is one of the main triggers, but governments and institutions have not prioritized the study of cancer's environmental connections. A detailed study shows a correlation between environmental pollution and cancer mortality, with higher mortality rates in highly polluted areas despite healthier lifestyles. The quality of air plays the most important role in influencing cancer mortality rates.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Medicine, General & Internal
Raffaella Massafra, Annarita Fanizzi, Nicola Amoroso, Samantha Bove, Maria Colomba Comes, Domenico Pomarico, Vittorio Didonna, Sergio Diotaiuti, Luisa Galati, Francesco Giotta, Daniele La Forgia, Agnese Latorre, Angela Lombardi, Annalisa Nardone, Maria Irene Pastena, Cosmo Maurizio Ressa, Lucia Rinaldi, Pasquale Tamborra, Alfredo Zito, Angelo Virgilio Paradiso, Roberto Bellotti, Vito Lorusso
Summary: Recently, machine learning and deep learning methods have been used to study breast cancer invasive disease events (IDEs), but their interpretability is poor. Therefore, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs in a cohort of 486 breast cancer patients. By using Shapley values, we identified the driving features for IDEs in two clinical periods of 5 and 10 years. The results showed that age, tumor diameter, surgery type, and multiplicity dominate the 5-year frame, while therapy-related features such as hormones and chemotherapy schemes, along with lymphovascular invasion, influence the prediction of IDEs in the 10-year period. Estrogen Receptor (ER), proliferation marker Ki67, and metastatic lymph nodes have an impact on both time frames. Our framework aims to bridge the gap between AI and clinical practice.
FRONTIERS IN MEDICINE
(2023)
Review
Business
Francesco De Nicolo, Loredana Bellantuono, Dario Borzi, Matteo Bregonzio, Roberto Cilli, Leone De Marco, Angela Lombardi, Ester Pantaleo, Luca Petruzzellis, Ariona Shashaj, Sabina Tangaro, Alfonso Monaco, Nicola Amoroso, Roberto Bellotti
Summary: Online reviews are important for decision-making and analyzing them accurately is crucial. Intelligent systems that utilize both textual and numerical reviews are necessary to understand and improve the tourist experience. This paper presents an eXplainable Artificial Intelligence framework that combines sentiment analysis and machine learning to accurately model and explain evaluations. The findings suggest caution when using review ratings and emphasize the importance of explainability in identifying key concepts in positive or negative ratings.
INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT
(2023)
Article
Environmental Sciences
Nicola Amoroso, Roberto Cilli, Davide Oscar Nitti, Raffaele Nutricato, Muzaffer Can Iban, Tommaso Maggipinto, Sabina Tangaro, Alfonso Monaco, Roberto Bellotti
Summary: PSI data are valuable for monitoring on-ground displacements. Clustering algorithms can be insufficient in capturing spatial constraints and revealing patterns at lower scales or possible anomalies. Therefore, we propose a novel framework that combines a spatially-constrained clustering algorithm (SKATER) with the LISA method for reliable anomaly detection. The workflow effectively identifies subsidence and uplifting in the study area, which is important for environmental and infrastructural purposes.
Article
Environmental Sciences
Andrea Tateo, Vincenzo Campanaro, Nicola Amoroso, Loredana Bellantuono, Alfonso Monaco, Ester Pantaleo, Rosaria Rinaldi, Tommaso Maggipinto
Summary: This study investigates how meteorological conditions can affect particulate matter (PM) concentrations. The findings show that air pollution levels are significantly associated with meteorological conditions and can be predicted using either ground weather observations or weather forecasts.
Article
Chemistry, Multidisciplinary
Nicola Corriero, Rosanna Rizzi, Gaetano Settembre, Nicoletta Del Buono, Domenico Diacono
Summary: CrystalMELA is a machine-learning-based web platform for crystal systems classification. Two different machine learning models, random forest and convolutional neural network, are available through the platform. The models were trained using simulated powder X-ray diffraction patterns of over 280,000 published crystal structures. The platform provides powerful and user-friendly classification options and can be accessed for free after registration.
JOURNAL OF APPLIED CRYSTALLOGRAPHY
(2023)
Article
Medicine, General & Internal
Maricla Marrone, Loredana Bellantuono, Alessandra Stellacci, Federica Misceo, Maria Silvestre, Fiorenza Zotti, Alessandro Dell'Erba, Roberto Bellotti
Summary: Haemorrhage refers to the loss of blood from damaged blood vessels. Determining the time of haemorrhage is challenging due to the poor correlation between systemic tissue perfusion and perfusion of specific tissues. This study aims to establish a precise time-of-death interval in cases of exsanguination following vascular injury, providing assistance in criminal investigations. A formula based on total blood volume and injured vessel calibre was developed to estimate the time interval of death from haemorrhage. The study model shows potential for future work and further analysis to identify corrective factors.
Article
Biochemistry & Molecular Biology
Antonio Lacalamita, Grazia Serino, Ester Pantaleo, Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Emanuele Piccinno, Viviana Scalavino, Francesco Dituri, Sabina Tangaro, Roberto Bellotti, Gianluigi Giannelli
Summary: This study proposes a supervised learning framework based on hierarchical community detection and artificial intelligence to classify patients and controls with hepatocellular carcinoma (HCC). Through the method, 20 gene communities were identified that can discriminate between healthy and cancerous samples with an accuracy exceeding 90%. The study also applied explainable artificial intelligence to analyze the contribution of each gene in two biologically relevant communities to the classification task.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Pietro Guccione, Domenico Diacono, Stefano Toso, Rocco Caliandro
Summary: The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations of nano or quasi-amorphous materials. However, ab initio crystal structural solution by the PDF is difficult due to the determination of crystallographic properties. This study presents a method for estimating crystal cell parameters directly from a PDF profile using machine-learning and multivariate analysis. The procedure is validated on known crystal structures and measured PDF profiles, achieving promising results.
Article
Multidisciplinary Sciences
Alessandro Fania, Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Roberto Cazzolla Gatti, Najada Firza, Antonio Lacalamita, Ester Pantaleo, Sabina Tangaro, Alena Velichevskaya, Roberto Bellotti
Summary: Dementia is a growing global public health priority, especially in Italy, where the number of elderly people is projected to increase significantly in the coming years. A dataset on mortality rates of Alzheimer's disease (AD) and Parkinson's disease (PD) in Italy over an 8-year period has been presented, which provides valuable information for health monitoring and research on new treatments and early diagnosis of dementia.
Article
Chemistry, Medicinal
Maria Vittoria Togo, Fabrizio Mastrolorito, Fulvio Ciriaco, Daniela Trisciuzzi, Anna Rita Tondo, Nicola Gambacorta, Loredana Bellantuono, Alfonso Monaco, Francesco Leonetti, Roberto Bellotti, Cosimo Damiano Altomare, Nicola Amoroso, Orazio Nicolotti
Summary: In this article, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is introduced for the prediction of developmental toxicity, which is a challenging human-health endpoint in toxicology. The use of XAI as an alternative method is highly important, as developmental toxicity is one of the most animal-intensive areas in regulatory toxicology.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
