Article
Computer Science, Artificial Intelligence
Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad
Summary: A new two-phase gene selection method for microarray data is proposed in this study. This method reduces the number of genes significantly and improves the classification accuracy through anomaly detection and guided genetic algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kushal Kanti Ghosh, Shemim Begum, Aritra Sardar, Sukdev Adhikary, Manosij Ghosh, Munish Kumar, Ram Sarkar
Summary: DNA microarray experiments provide information about cell and tissue states, with only a few genes playing a significant role in disease classification. Feature selection algorithms aim to efficiently identify relevant features, with feature ranking techniques assigning importance to features without using learning algorithms. This paper extensively studies 10 popular filter ranking methods and their performance on various microarray datasets using different classifiers. The experiments show that Mutual Information is the most effective method among Entropy based methods, ReliefF is best in the Similarity based methods category, and Chi-square performs well in the Statistics based methods category.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Computer Science, Artificial Intelligence
Mahnaz Vahmiyan, Mohammadtaghi Kheirabadi, Ebrahim Akbari
Summary: Feature selection is crucial in medicine and genetics research, especially in high-dimensional data. This paper provides a systematic survey of studies on FS techniques in microarrays, with results highlighting the importance of classification and the wide application of evolutionary methods in FS.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Hasna Chamlal, Tayeb Ouaderhman, Fatima Ezzahra Rebbah
Summary: The feature selection process is crucial in various fields, especially in bioinformatics and microarray gene expression data analysis. This study introduces a new feature selection method that can handle high-dimensional data and effectively select features in large-scale gene datasets.
INFORMATION SCIENCES
(2022)
Article
Engineering, Chemical
Waleed Ali, Faisal Saeed
Summary: Advancements in intelligent systems have greatly contributed to the fields of bioinformatics, health, and medicine. This paper proposes a hybrid filter-genetic feature selection approach to improve the performance of cancer classification by addressing the high-dimensionality and noisy nature of microarray data. Experimental results demonstrate that the proposed method outperforms common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.
Review
Computer Science, Artificial Intelligence
Sarah Osama, Hassan Shaban, Abdelmgeid A. Ali
Summary: This review explores the applications of machine learning-based data reduction and classification algorithms in microarray gene expression data. It summarizes various data preprocessing methods, reviews different feature selection algorithms, and discusses feature extraction and hybrid methods. It also examines widely used machine learning algorithms for tumor and nontumor classification. Finally, the challenges and unanswered questions in accurate cancer classification and detection are highlighted.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Bruno I. Grisci, Bruno Cesar Feltes, Joice de Faria Poloni, Pedro H. Narloch, Marcio Dorn
Summary: A review of over 1200 publications on feature selection and gene expression between 2010 and 2020 found that 57% of the publications used outdated datasets, 23% used only outdated data, and 32% did not cite data sources. Problems such as referencing unavailable databases, slow adoption of RNA-seq datasets, and bias towards human cancer data were also identified. These issues can result in inaccurate and misleading biological results.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Computer Science, Artificial Intelligence
Mehrdad Rostami, Saman Forouzandeh, Kamal Berahmand, Mina Soltani, Meisam Shahsavari, Mourad Oussalah
Summary: The proposed social network analysis-based gene selection approach aims to maximize relevance and minimize redundancy of selected genes by repetitively selecting maximum communities and using node centrality-based criteria. This method improves classification accuracy of microarray data while reducing time complexity.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Biology
Francois Signol, Laura Arnal, J. Ramon Navarro-Cerdan, Rafael Llobet, Joaquim Arlandis, Juan-Carlos Perez-Cortes
Summary: This paper introduces a feature identification algorithm called SEQENS, which is capable of identifying relevant variables in a case-control study using a genetic expression microarray dataset. SEQENS uses Sequential Feature Search on multiple sample splitting to select variables showing stronger relation with the target, and produces a variable relevance ranking. The algorithm is compared with other state-of-the-art methods and performs better in identifying relevant genes with higher stability.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Vahid Nosrati, Mohsen Rahmani
Summary: This paper presents a novel framework, named feature-level aggregation-based ensemble based on overlapped feature subspace partitioning (FLAE-OFSP), for microarray data classification. The proposed ensemble generates multiple subsets and applies feature selection algorithms to each subset, and the results are combined into a single ranked list. Evaluation on seven microarray datasets shows significant improvement in runtime and quality results compared to individual methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Weichan Zhong, Xiaojun Chen, Feiping Nie, Joshua Zhexue Huang
Summary: In this paper, a novel adaptive discriminant analysis method SADA is proposed for semi-supervised feature selection, which can effectively learn the similarity matrix and projection matrix during the process. Experimental results demonstrate the superior performance of SADA compared to other semi-supervised feature selection methods.
INFORMATION SCIENCES
(2021)
Article
Biochemical Research Methods
Fahimeh Motamedi, Horacio Perez-Sanchez, Alireza Mehridehnavi, Afshin Fassihi, Fahimeh Ghasemi
Summary: This article discusses two approaches for quantitative structure-activity prediction studies, focusing on identifying appropriate molecular descriptors and predicting the biological activities of designed compounds. The use of LASSO-random forest algorithm is shown to significantly improve output correlation, reduce implementation time and model complexity, while maintaining prediction accuracy.
Article
Biochemical Research Methods
Kun Yu, Weidong Xie, Linjie Wang, Wei Li
Summary: The proposed feature selection algorithm in the study outperformed other methods in microarray data analysis, showing higher stability and classification accuracy. The selected biomarkers also matched the clinical data provided by the cooperative hospital.
BMC BIOINFORMATICS
(2021)
Article
Engineering, Biomedical
Weidong Xie, Linjie Wang, Kun Yu, Tengfei Shi, Wei Li
Summary: Gene microarray technology plays a crucial role in disease diagnosis. In this paper, an improved multilayer binary firefly-based method is proposed to reduce data dimensionality and optimize feature space. Experimental results show that the method achieves higher classification accuracy with fewer features.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Review
Computer Science, Information Systems
Utkarsh Mahadeo Khaire, R. Dhanalakshmi
Summary: Feature selection technique is a tool for understanding problems by analyzing relevant features, which can improve classifier performance and reduce computational load. However, the high correlation between features often leads to instability in traditional feature selection algorithms, resulting in reduced confidence in the selected features. Therefore, achieving high stability in feature selection algorithms is crucial.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Oncology
Anders Berglund, Clarisse Muenyi, Erin M. Siegel, Abidemi Ajidahun, Steven A. Eschrich, Denise Wong, Leah E. Hendrick, Ryan M. Putney, Sungjune Kim, D. Neil Hayes, David Shibata
Summary: HPV16 and other HPV types within the same alpha species exhibit distinct methylation profiles in head and neck squamous cell carcinomas (HNSCC). Understanding these differences could provide insights into the clinical behavior of HPV-associated HNSCC and contribute to a better understanding of the biological mechanisms underlying HPV-related tumors.
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
(2022)
Article
Oncology
Jaileene Perez-Morales, Hong Lu, Wei Mu, Ilke Tunali, Tugce Kutuk, Steven A. Eschrich, Yoganand Balagurunathan, Robert J. Gillies, Matthew B. Schabath
Summary: This study used radiomic features and volume doubling time (VDT) to identify high-risk subsets of lung cancer patients diagnosed in lung cancer screening. These vulnerable patients were associated with poor survival outcomes and may require more aggressive surveillance and treatment.
Article
Oncology
G. Daniel Grass, Juan C. L. Alfonso, Eric Welsh, Kamran A. Ahmed, Jamie K. Teer, Shari Pilon-Thomas, Louis B. Harrison, John L. Cleveland, James J. Mule, Steven A. Eschrich, Heiko Enderling, Javier F. Torres-Roca
Summary: This study used the radiation sensitivity index (RSI) gene signature to estimate the sensitivity of tumors to radiation therapy and characterized their immune microenvironments. The results showed that tumors sensitive to radiation therapy exhibited enrichment of interferon-associated signaling pathways and immune cell infiltrates.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Rafael Rivera-Lopez, Juana Canul-Reich, Efren Mezura-Montes, Marco Antonio Cruz-Chavez
Summary: This paper presents a state-of-the-art review of using single-solution-based metaheuristics and swarm and evolutionary computational algorithms to build decision trees as classification models, outlining the decision-tree induction process components and detailing existing literature studies on metaheuristic-based approaches to building these classifiers. A summary analysis of these studies is also conducted, focusing on their internal components and experimental studies.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Cell Biology
Yainyrette Rivera-Rivera, Geraldine Vargas, Neha Jaiswal, Angel Nunez-Marrero, Jiannong Li, Dung-Tsa Chen, Steven Eschrich, Marilin Rosa, Joseph O. Johnson, Julie Dutil, Srikumar P. Chellappan, Harold Saavedra
Summary: Molecular epidemiological evidence suggests that racial and ethnic differences play a role in the aggressiveness and survival of breast cancer. The study reveals that the mitotic kinases TTK, TBK1, and Nek2 could be potential targets for preventing breast cancer progression in Hispanics/Latinas and non-Hispanic Black women. These findings indicate the importance of understanding molecular factors in different racial and ethnic populations.
Article
Computer Science, Artificial Intelligence
Henry Jesus Hernandez-Gomez, Juana Canul-Reich, Betania Hernandez-Ocana, Erick de la Cruz Hernandez
Summary: The aim of this study was to explore a dataset of patients with Bacterial Vaginosis (BV) in order to determine the optimal number of patient groups for further analysis. The Agglomerative Hierarchical Clustering (AHC) algorithm was applied to the dataset from an urban population in southeastern Mexico. The results identified distinct groups of patients with BV and showed that the AHC algorithm can be used to analyze the complex dynamics of bacteria in these groups.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Biochemical Research Methods
Alyssa N. Obermayer, Darwin Chang, Gabrielle Nobles, Mingxiang Teng, Aik-Choon Tan, Xuefeng Wang, Y. Ann Chen, Steven Eschrich, Paulo C. Rodriguez, G. Daniel Grass, Soheil Meshinchi, Ahmad Tarhini, Dung-tsa Chen, Timothy I. Shaw
Summary: PATH-SURVEYOR is a comprehensive suite for pathway-level survival analysis, allowing systematic exploration of pathways and covariates in a Cox proportional-hazard model. The tool identified immune populations and biomarkers predictive of treatment efficacy in melanoma patients and drug targets in high-risk pediatric acute myeloid leukemia patients.
BMC BIOINFORMATICS
(2023)
Article
Oncology
Javier F. Torres-Roca, G. Daniel Grass, Jacob G. Scott, Steven A. Eschrich
Summary: The genomic era has revolutionized clinical oncology, with the use of genomics for guiding decisions on chemotherapy and targeted therapies becoming routine. However, the use of genomics in radiation therapy decisions remains limited, despite the recognition of tumor heterogeneity.
SEMINARS IN RADIATION ONCOLOGY
(2023)
Article
Oncology
George Daniel Grass, Dalia Ercan, Alyssa N. Obermayer, Timothy Shaw, Paul A. Stewart, Jad Chahoud, Jasreman Dhillon, Alex Lopez, Peter A. S. Johnstone, Silvia Regina Rogatto, Philippe E. Spiess, Steven A. Eschrich
Summary: This study provides the first description of the surfaceome in penile cancer and evaluates the impact of human papillomavirus (HPV) infection on surfaceome diversity. The analysis found a diverse surfaceome within patient tumors, with a high proportion of membrane proteins and transporters. The study also identified significant differences in protein classes based on HPV status and a prognostic immunoglobulin protein called BSG/CD147.
Review
Oncology
Kevin Parza, Arfa Mustasam, Filip Ionescu, Mahati Paravathaneni, Reagan Sandstrom, Houssein Safa, G. Daniel Grass, Peter A. Johnstone, Steven A. Eschrich, Juskaran Chadha, Niki Zacharias, Curtis A. Pettaway, Philippe E. Spiess, Jad Chahoud
Summary: This paper systematically reviewed the literature on the impact of HPV and p16 immunohistochemistry on the prognosis of PSCC. The study found that HPV-positive and p16-positive PSCC patients had better overall survival and disease-free survival. This highlights the need for a meta-analysis to determine the role of routine HPV status or p16 staining testing in the initial diagnosis and staging of PSCC patients worldwide.
Article
Multidisciplinary Sciences
John Heine, Erin E. E. Fowler, Anders Berglund, Michael J. Schell, Steven Eschrich
Summary: Data modeling requires a sufficient sample size for reproducibility, but a small sample size can hinder model evaluation. This study evaluates a synthetic data generation technique that addresses the small sample size problem. The technique generates synthetic data from a subgroup (class) with a latent multivariate normal characteristic and uses univariate kernel density estimation (KDE) to generate synthetic data. The generated synthetic samples are statistically similar to their respective samples. This approach offers a solution to the small sample size problem for a class of samples with a latent normal characteristic.
SCIENTIFIC REPORTS
(2023)
Article
Mathematics, Interdisciplinary Applications
Maria Concepcion Salvador-Gonzalez, Juana Canul-Reich, Rafael Rivera-Lopez, Efren Mezura-Montes, Erick de la Cruz-Hernandez
Summary: Bacterial Vaginosis is a common and recurring public health issue that can lead to other sexually transmitted diseases. This study uses a dataset of sexually active women aged 18 to 50, categorizing 17 numerical attributes of microorganisms and bacteria with positive and negative BV results using the Apriori algorithm. Statistical metrics such as support, confidence, and lift are used to evaluate the quality of the association rules, which are then incorporated into the objective function of the DE algorithm. The results allowed for the selection of a reduced subset of biologically meaningful association rules.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2023)
Meeting Abstract
Oncology
Sudhir Putty Reddy, Aileen Alontaga, Paul A. Stewart, Eric Welsh, Lamees Saeed, Lancia N. F. Darville, Bin Fang, Steven A. Eschrich, Eric B. Haura, Theresa A. Boyle, John M. Koomen
Meeting Abstract
Oncology
A. Patel, R. Jain, R. Li, A. J. Sim, E. Welsh, S. A. Eschrich, K. A. Ahmed, D. Grass
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Meeting Abstract
Oncology
Samer A. Naffouje, Barbara Centeno, Steven Eschrich, Jamie Teer, Sameh Elessawy, Ola Gaber, Pamela Hodul, Jason Fleming, Jason Denbo, Daniel Anaya, Jose M. Pimiento, Estrella Carballido, Dae Won Kim, Aamir Dam, Surinder Batra, Maneesh Jain, Robert Gatenby, Mokenge Malafa
ANNALS OF SURGICAL ONCOLOGY
(2022)