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Zehetner L, Széliová D, Kraus B, Hernandez Bort JA, Zanghellini J. Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Comput Biol 2024; 20:e1012236. [PMID: 38913731 PMCID: PMC11226097 DOI: 10.1371/journal.pcbi.1012236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
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Affiliation(s)
- Leopold Zehetner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Diana Széliová
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Barbara Kraus
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Juan A. Hernandez Bort
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
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2
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Madhan S, Kalaiselvan A. Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. NETWORK (BRISTOL, ENGLAND) 2024:1-25. [PMID: 38736309 DOI: 10.1080/0954898x.2024.2348726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024]
Abstract
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.
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Affiliation(s)
- Subramaniam Madhan
- Department of Computer Science and Engineering, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
| | - Anbarasan Kalaiselvan
- Department of Science and Humanities, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
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3
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Brooks TG, Lahens NF, Mrčela A, Grant GR. Challenges and best practices in omics benchmarking. Nat Rev Genet 2024; 25:326-339. [PMID: 38216661 DOI: 10.1038/s41576-023-00679-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2023] [Indexed: 01/14/2024]
Abstract
Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.
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Affiliation(s)
- Thomas G Brooks
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas F Lahens
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Antonijo Mrčela
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory R Grant
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
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Li S, Liu S, Sun X, Hao L, Gao Q. Identification of endocrine-disrupting chemicals targeting key DCM-associated genes via bioinformatics and machine learning. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 274:116168. [PMID: 38460409 DOI: 10.1016/j.ecoenv.2024.116168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 03/11/2024]
Abstract
Dilated cardiomyopathy (DCM) is a primary cause of heart failure (HF), with the incidence of HF increasing consistently in recent years. DCM pathogenesis involves a combination of inherited predisposition and environmental factors. Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with endogenous hormone action and are capable of targeting various organs, including the heart. However, the impact of these disruptors on heart disease through their effects on genes remains underexplored. In this study, we aimed to explore key DCM-related genes using machine learning (ML) and the construction of a predictive model. Using the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) and performed enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to DCM. Through ML techniques combining maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key genes for predicting DCM (IL1RL1, SEZ6L, SFRP4, COL22A1, RNASE2, HB). Based on these key genes, 79 EDCs with the potential to affect DCM were identified, among which 4 (3,4-dichloroaniline, fenitrothion, pyrene, and isoproturon) have not been previously associated with DCM. These findings establish a novel relationship between the EDCs mediated by key genes and the development of DCM.
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Affiliation(s)
- Shu Li
- Department of Health and Intelligent Engineering, College of Health Management, China Medical University, Shenyang, Liaoning Province 110122, PR China..
| | - Shuice Liu
- Department of Pharmacology, Shenyang Medical College, Shenyang, Liaoning Province 110001, PR China..
| | - Xuefei Sun
- Department of Pharmaceutical Toxicology, School of Pharmacy, China Medical University, Shenyang 110122, PR China..
| | - Liying Hao
- Department of Pharmaceutical Toxicology, School of Pharmacy, China Medical University, Shenyang 110122, PR China..
| | - Qinghua Gao
- Department of Developmental Cell Biology, Key Laboratory of Cell Biology, Ministry of Public Health, and Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, PR China..
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Liu X, Fu B, Chen J, Sun Z, Zheng D, Li Z, Gu B, Zhang Y, Lu H. High-throughput intact Glycopeptide quantification strategy with targeted-MS (HTiGQs-target) reveals site-specific IgG N-glycopeptides as biomarkers for hepatic disorder diagnosis and staging. Carbohydr Polym 2024; 325:121499. [PMID: 38008487 DOI: 10.1016/j.carbpol.2023.121499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/28/2023]
Abstract
Liver disease is one of the leading causes of global mortality, and identifying biomarkers for diagnosing the progression of liver diseases is crucial for improving its outcomes. Targeted mass spectrometry technology is a powerful tool with unique advantages for verifying biomarker candidates and clinical applications. It is particularly useful in validating protein biomarkers with post-translational modifications, eliminating the need for site-specific antibodies. Especially, targeted mass spectrometry technique is particularly critical for translation of glycoproteins into clinical applications as there are no site-specific antibodies for N-glycosylation. Nevertheless, its limitation in analyzing only one sample per run has become apparent when dealing with a large number of clinical samples. Herein, we developed a high-throughput intact N-glycopeptides quantification strategy with targeted-MS (HTiGQs-Target), which allows the validation of 20 samples per run with an average analysis time of only 3 min per sample. We applied HTiGQs-Target in a cohort of 461 serum samples (including 120 healthy controls (HC), 127 chronic hepatitis B (CHB) cases, 106 liver cirrhosis (LC) cases, and 108 hepatocellular carcinomas (HCC) cases) and found that a panel of 10 IgG N-glycopeptides have strong clinical utility in evaluating the severity of the liver disease.
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Affiliation(s)
- Xuejiao Liu
- Liver Cancer Institute of Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Bin Fu
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Jierong Chen
- Laboratory Medicine of Guangdong Provincial People's Hospital and Guangdong, Academy of Medical Sciences, Guangzhou, Guangdong 510000, China
| | - Zhenyu Sun
- Liver Cancer Institute of Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Dongdong Zheng
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Zhonghua Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Bing Gu
- Laboratory Medicine of Guangdong Provincial People's Hospital and Guangdong, Academy of Medical Sciences, Guangzhou, Guangdong 510000, China.
| | - Ying Zhang
- Liver Cancer Institute of Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China.
| | - Haojie Lu
- Liver Cancer Institute of Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China.
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Tukacs V, Mittli D, Hunyadi-Gulyás É, Darula Z, Juhász G, Kardos J, Kékesi KA. Comparative analysis of hippocampal extracellular space uncovers widely altered peptidome upon epileptic seizure in urethane-anaesthetized rats. Fluids Barriers CNS 2024; 21:6. [PMID: 38212833 PMCID: PMC10782730 DOI: 10.1186/s12987-024-00508-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/31/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND The brain extracellular fluid (ECF), composed of secreted neurotransmitters, metabolites, peptides, and proteins, may reflect brain processes. Analysis of brain ECF may provide new potential markers for synaptic activity or brain damage and reveal additional information on pathological alterations. Epileptic seizure induction is an acute and harsh intervention in brain functions, and it can activate extra- and intracellular proteases, which implies an altered brain secretome. Thus, we applied a 4-aminopyridine (4-AP) epilepsy model to study the hippocampal ECF peptidome alterations upon treatment in rats. METHODS We performed in vivo microdialysis in the hippocampus for 3-3 h of control and 4-AP treatment phase in parallel with electrophysiology measurement. Then, we analyzed the microdialysate peptidome of control and treated samples from the same subject by liquid chromatography-coupled tandem mass spectrometry. We analyzed electrophysiological and peptidomic alterations upon epileptic seizure induction by two-tailed, paired t-test. RESULTS We detected 2540 peptides in microdialysate samples by mass spectrometry analysis; and 866 peptides-derived from 229 proteins-were found in more than half of the samples. In addition, the abundance of 322 peptides significantly altered upon epileptic seizure induction. Several proteins of significantly altered peptides are neuropeptides (Chgb) or have synapse- or brain-related functions such as the regulation of synaptic vesicle cycle (Atp6v1a, Napa), astrocyte morphology (Vim), and glutamate homeostasis (Slc3a2). CONCLUSIONS We have detected several consequences of epileptic seizures at the peptidomic level, as altered peptide abundances of proteins that regulate epilepsy-related cellular processes. Thus, our results indicate that analyzing brain ECF by in vivo microdialysis and omics techniques is useful for monitoring brain processes, and it can be an alternative method in the discovery and analysis of CNS disease markers besides peripheral fluid analysis.
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Affiliation(s)
- Vanda Tukacs
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
| | - Dániel Mittli
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
| | - Éva Hunyadi-Gulyás
- Laboratory of Proteomics Research, Biological Research Centre, Hungarian Research Network (HUN-REN), Temesvári Körút 62, Szeged, 6726, Hungary
| | - Zsuzsanna Darula
- Laboratory of Proteomics Research, Biological Research Centre, Hungarian Research Network (HUN-REN), Temesvári Körút 62, Szeged, 6726, Hungary
- Single Cell Omics Advanced Core Facility, Hungarian Centre of Excellence for Molecular Medicine, Temesvári Körút 62, Szeged, 6726, Hungary
| | - Gábor Juhász
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
- InnoScience Hungary Ltd., Bátori Út 9, Mátranovák, 3142, Hungary
| | - József Kardos
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary
| | - Katalin Adrienna Kékesi
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary.
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary.
- InnoScience Hungary Ltd., Bátori Út 9, Mátranovák, 3142, Hungary.
- Department of Physiology and Neurobiology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter Sétány 1/C, Budapest, 1117, Hungary.
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Tasci E, Jagasia S, Zhuge Y, Camphausen K, Krauze AV. GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics. Cancers (Basel) 2023; 15:4628. [PMID: 37760597 PMCID: PMC10526509 DOI: 10.3390/cancers15184628] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/01/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.
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Affiliation(s)
| | | | | | | | - Andra Valentina Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
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Kuzudisli C, Bakir-Gungor B, Bulut N, Qaqish B, Yousef M. Review of feature selection approaches based on grouping of features. PeerJ 2023; 11:e15666. [PMID: 37483989 PMCID: PMC10358338 DOI: 10.7717/peerj.15666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 06/08/2023] [Indexed: 07/25/2023] Open
Abstract
With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly-ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work's findings can guide effective design of new FS approaches using feature grouping.
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Affiliation(s)
- Cihan Kuzudisli
- Department of Computer Engineering, Hasan Kalyoncu University, Gaziantep, Turkey
- Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Nurten Bulut
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Bahjat Qaqish
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, Chapel Hill, United States of America
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel
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Hapfelmeier A, Hornung R, Haller B. Efficient permutation testing of variable importance measures by the example of random forests. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2022.107689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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