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Altman MB, Wan W, Hosseini AS, Arabi Nowdeh S, Alizadeh M. Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review. Heliyon 2024; 10:e26652. [PMID: 38434008 PMCID: PMC10906441 DOI: 10.1016/j.heliyon.2024.e26652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/09/2024] [Accepted: 02/16/2024] [Indexed: 03/05/2024] Open
Abstract
Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications.
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Affiliation(s)
- Morteza Babaee Altman
- Department of Energy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 1591634311, Iran
| | - Wenbin Wan
- Department of Mechanical Engineering, University of New Mexico, MSC01 1150, Albuquerque, NM 87131, USA
| | - Amineh Sadat Hosseini
- Department of Electrical and Biomedical Engineering, Islamic Azad University, Golestan, Iran
| | | | - Masoumeh Alizadeh
- Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, 1591634311,Iran
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Zhang X, Zhang H, Wang Z, Ma X, Luo J, Zhu Y. PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping. BMC Bioinformatics 2023; 24:490. [PMID: 38129803 PMCID: PMC10740247 DOI: 10.1186/s12859-023-05595-4] [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: 06/07/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Clustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC). RESULTS The PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e-05), while using the Calinski-Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/ . CONCLUSIONS PWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.
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Affiliation(s)
- Xiaomeng Zhang
- Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China
| | - Hongtao Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Zhihao Wang
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Xiaofei Ma
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Jiancheng Luo
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China.
| | - Yingying Zhu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China.
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Zelig A, Kariti H, Kaplan N. KMD clustering: robust general-purpose clustering of biological data. Commun Biol 2023; 6:1110. [PMID: 37919399 PMCID: PMC10622433 DOI: 10.1038/s42003-023-05480-z] [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: 08/02/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023] Open
Abstract
The noisy and high-dimensional nature of biological data has spawned advanced clustering algorithms that are tailored for specific biological datatypes. However, the performance of such methods varies greatly between datasets and they require post hoc tuning of cryptic hyperparameters. We present k minimal distance (KMD) clustering, a general-purpose method based on a generalization of single and average linkage hierarchical clustering. We introduce a generalized silhouette-like function to eliminate the cryptic hyperparameter k, and use sampling to enable application to million-object datasets. Rigorous comparisons to general and specialized clustering methods on simulated, mass cytometry and scRNA-seq datasets show consistent high performance of KMD clustering across all datasets.
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Affiliation(s)
- Aviv Zelig
- Data Science & Engineering Program, Faculty of Industrial Engineering & Management, Technion - Israel Institute of Technology, Haifa, Israel
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - Hagai Kariti
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - Noam Kaplan
- Department of Physiology, Biophysics & Systems Biology, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.
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Du M, Mo Y, Li A, Ge S, Peres MA. Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis. J Periodontol 2023; 94:1254-1265. [PMID: 37133974 DOI: 10.1002/jper.23-0088] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/13/2023] [Accepted: 04/15/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND The performance of the 2018 European Federation of Periodontology/American Academy of Periodontology (EFP/AAP) classification of periodontitis for epidemiology surveillance purposes remains to be investigated. This study assessed the surveillance use of the 2018 EFP/AAP classification and its agreement with the unsupervised clustering method compared with the 2012 Centers for Disease Control and Prevention(CDC)/AAP case definition. METHODS Participants (n = 9424) in the National Health and Nutrition Examination Survey (NHANES) were staged by the 2018 EFP/AAP classification and classified into subgroups via k-medoids clustering. Concordance levels between periodontitis definitions and the clustering method were evaluated through the multiclass area under the receiver operating characteristic curve (multiclass AUC) among "periodontitis cases" and the general population, respectively. The multiclass AUC of the 2012 CDC/AAP definition versus clustering was used as a reference. The associations of periodontitis with chronic diseases were estimated using multivariable logistic regression. RESULTS All the participants were identified as "periodontitis cases" by the 2018 EFP/AAP classification, and the prevalence of stage III-IV was 30%. The optimal numbers of clusters were three and four. The 2012 CDC/AAP definition versus clustering yielded a multiclass AUC of 0.82 and 0.85 among the general population and "periodontitis cases," respectively. The multiclass AUC of the 2018 EFP/AAP classification versus clustering was 0.77 and 0.78 for different target populations. Similar patterns prevailed in associations with chronic diseases between the 2018 EFP/AAP classification and clustering. CONCLUSIONS The validity of the 2018 EFP/AAP classification was verified by the unsupervised clustering method, which performed better in distinguishing "periodontitis cases" than classifying the general population. For surveillance purposes, the 2012 CDC/AAP definition showed a higher agreement level with the clustering method than the 2018 EFP/AAP classification.
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Affiliation(s)
- Mi Du
- Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Yuanqiu Mo
- Department of System Science, School of Mathematics, Southeast University, Nanjing, China
| | - An Li
- Department of Periodontology, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Shaohua Ge
- Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Marco A Peres
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore
- Oral Health ACP, Health Service and Systems Research Programme, Duke-NUS Medical School, Singapore
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Schulze HG, Rangan S, Vardaki MZ, Blades MW, Turner RFB, Piret JM. Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra. APPLIED SPECTROSCOPY 2023; 77:835-847. [PMID: 36238996 PMCID: PMC10466967 DOI: 10.1177/00037028221133851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.
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Affiliation(s)
| | - Shreyas Rangan
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Martha Z. Vardaki
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Michael W. Blades
- Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F. B. Turner
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - James M. Piret
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
- Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
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Ichihara M, Ohminato T, Konstantinou KI, Yamakawa K, Watanabe A, Takeo M. Seismic background level (SBL) growth can reveal slowly developing long-term eruption precursors. Sci Rep 2023; 13:5954. [PMID: 37045890 PMCID: PMC10097692 DOI: 10.1038/s41598-023-32875-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
The accelerating growth of seismic unrest before eruptions has been observed at many volcanoes and utilized for eruption forecasts. However, there are still many eruptions for which no precursory unrest has been identified, even at well-monitored volcanoes. The recent eruptions of Shinmoe-dake, Japan, have been another negative example of this kind. Here we present seismological evidence that the eruption preparation had been ongoing at the shallow depths beneath Shinmoe-dake for several months to a year. We investigated the seismic background level (SBL) of eleven-year data recorded around the volcano, including two stations about 1 km from the eruptive crater. We searched for persistent weak signals, focusing on low-amplitude time windows recorded during quiet nighttime. Then the spectra of daily background noise were classified by clustering analysis. The SBL analysis successfully revealed very weak precursory tremors from more than several months before the eruption, and residual tremors to the end of the eruptive period. The precursory signals grew acceleratory in a similar way as is assumed in the material failure forecast method applied to eruption forecasts. However, their growth was significantly slower and longer compared to other cases reported in the literature. Such slow and quiet eruption preparations would not be captured by conventional seismological methods. We expect that long-term SBL analyses on proximal seismic data will help detect early precursors, even at seismically quiet volcanoes, and will also help towards judging the end of an eruptive period.
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Affiliation(s)
- Mie Ichihara
- Earthquake Research Institute, University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-0032, Japan.
| | - Takao Ohminato
- Earthquake Research Institute, University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Kostas I Konstantinou
- Department of Earth Sciences, National Central University, Jhongli, 320, Taoyuan, Taiwan
| | - Kazuya Yamakawa
- Mount Fuji Research Institute Yamanashi Prefectural Government, 5597-1 Kenmarubi, Kamiyoshida, Fujiyoshida, Yamanashi, 403-0005, Japan
| | - Atsushi Watanabe
- Earthquake Research Institute, University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Minoru Takeo
- Earthquake Research Institute, University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-0032, Japan
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7
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Esnault C, Rollot M, Guilmin P, Zucker JD. Qluster: An easy-to-implement generic workflow for robust clustering of health data. Front Artif Intell 2023; 5:1055294. [PMID: 36814808 PMCID: PMC9939832 DOI: 10.3389/frai.2022.1055294] [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: 09/27/2022] [Accepted: 12/22/2022] [Indexed: 02/08/2023] Open
Abstract
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.
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Affiliation(s)
- Cyril Esnault
- Quinten, Paris, France,*Correspondence: Cyril Esnault ✉
| | | | | | - Jean-Daniel Zucker
- Sorbonne University, IRD, UMMISCO, Bondy, France,Sorbonne University, INSERM, NUTRIOMICS, Paris, France
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Howlett-Prieto Q, Oommen C, Carrithers MD, Wunsch DC, Hier DB. Subtypes of relapsing-remitting multiple sclerosis identified by network analysis. Front Digit Health 2023; 4:1063264. [PMID: 36714613 PMCID: PMC9874946 DOI: 10.3389/fdgth.2022.1063264] [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: 10/06/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.
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Affiliation(s)
- Quentin Howlett-Prieto
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Chelsea Oommen
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Michael D. Carrithers
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Donald C. Wunsch
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States
| | - Daniel B. Hier
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States,Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States,Correspondence: Daniel B. Hier
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Coker ES, Molitor J, Liverani S, Martin J, Maranzano P, Pontarollo N, Vergalli S. Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy. ENVIRONMENTAL RESEARCH 2023; 216:114484. [PMID: 36220446 PMCID: PMC9547389 DOI: 10.1016/j.envres.2022.114484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/23/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.
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Affiliation(s)
- Eric S Coker
- Department of Environmental and Global Health, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, United States.
| | - John Molitor
- College of Public Health and Human Sciences, Oregon State University, Milam Hall 157, 2520 SW Campus Way, Corvallis, OR, 97331, United States.
| | - Silvia Liverani
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road London E1 4NS, United Kingdom.
| | - James Martin
- Department of Environmental and Global Health, University of Florida, 1225 Center Dr, Gainesville, FL, 32610, United States
| | - Paolo Maranzano
- Department of Economics, Management and Statistics of the University of Milano-Bicocca (UniMiB), Piazza Dell'Ateneo Nuovo, 1 - 20126, Milano, Italy.
| | - Nicola Pontarollo
- Department of Economics and Management, Università Degli Studi di Brescia, Brescia, Via S. Faustino 74/B, 25122, Brescia, Italy.
| | - Sergio Vergalli
- Department of Agricultural Economics, Università Cattolica Del Sacro Cuore, Piacenza, Via Emilia Parmense, 29122, Piacenza PC, Italy.
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Shojaee Z, Shahzadeh Fazeli SA, Abbasi E, Adibnia F, Masuli F, Rovetta S. A Mutual Information Based on Ant Colony Optimization Method to Feature Selection for Categorical Data Clustering. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, TRANSACTIONS A: SCIENCE 2022. [DOI: 10.1007/s40995-022-01395-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Oyewole GJ, Thopil GA. Data clustering: application and trends. Artif Intell Rev 2022; 56:6439-6475. [PMID: 36466764 PMCID: PMC9702941 DOI: 10.1007/s10462-022-10325-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 11/28/2022]
Abstract
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.
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Affiliation(s)
- Gbeminiyi John Oyewole
- Department of Engineering and Technology Management, University of Pretoria, Pretoria, South Africa
| | - George Alex Thopil
- Department of Engineering and Technology Management, University of Pretoria, Pretoria, South Africa
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12
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Wu Y, Rahman MH. Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4711244. [PMID: 38283724 PMCID: PMC10821803 DOI: 10.1155/2022/4711244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/08/2022] [Indexed: 01/30/2024]
Abstract
In the field of biomedicine, enormous data are generated in a structured and unstructured form every day. Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies. The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making. The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data. A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes. Fuzzy logic and C-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions. This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions. Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives. The COVID-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people. Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields.
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Affiliation(s)
- Yanping Wu
- Hangzhou Medical College, Hangzhou 311399, China
| | - Md. Habibur Rahman
- Department of Information and Communication Technology, Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh, Gazipur 1750, Bangladesh
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Marzano L, Darwich AS, Tendler S, Dan A, Lewensohn R, De Petris L, Raghothama J, Meijer S. A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study. Clin Transl Sci 2022; 15:2437-2447. [PMID: 35856401 PMCID: PMC9579402 DOI: 10.1111/cts.13371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 01/25/2023] Open
Abstract
In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.
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Affiliation(s)
- Luca Marzano
- Division of Health Informatics and LogisticsSchool of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of TechnologyHuddingeSweden
| | - Adam S. Darwich
- Division of Health Informatics and LogisticsSchool of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of TechnologyHuddingeSweden
| | - Salomon Tendler
- Department of Oncology‐PathologyKarolinska Institutet and the Thoracic Oncology Center, Karolinska University HospitalStockholmSweden
| | - Asaf Dan
- Department of Oncology‐PathologyKarolinska Institutet and the Thoracic Oncology Center, Karolinska University HospitalStockholmSweden
| | - Rolf Lewensohn
- Department of Oncology‐PathologyKarolinska Institutet and the Thoracic Oncology Center, Karolinska University HospitalStockholmSweden
| | - Luigi De Petris
- Department of Oncology‐PathologyKarolinska Institutet and the Thoracic Oncology Center, Karolinska University HospitalStockholmSweden
| | - Jayanth Raghothama
- Division of Health Informatics and LogisticsSchool of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of TechnologyHuddingeSweden
| | - Sebastiaan Meijer
- Division of Health Informatics and LogisticsSchool of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of TechnologyHuddingeSweden
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14
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Chen D, Zhang C, Zang Y, Wang W, Zhang J. Identification of an immune-related gene prognostic index for predicting survival and immunotherapy efficacy in papillary renal cell carcinoma. Front Genet 2022; 13:970900. [PMID: 36159976 PMCID: PMC9499392 DOI: 10.3389/fgene.2022.970900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/07/2022] [Indexed: 12/02/2022] Open
Abstract
Despite considerable progress has been made in the understanding of the genetics and molecular biology of renal cell carcinoma (RCC), therapeutic options of patients with papillary renal cell carcinoma (PRCC) are limited. Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a hot point in researching new drug for tumor and been tested in a number of human clinical trials. In this study, an immune-related gene prognostic index (IRGPI) was developed and provided a comprehensive and systematic analysis of distinct phenotypic and molecular portraits in the recognition, surveillance, and prognosis of PRCC. The reliability of the IRGPI was evaluated using independent datasets from GEO database and the expression levels of the genes in the IRGPI detected by real-time PCR. Collectively, the currently established IRGPI could be used as a potential biomarker to evaluate the response and efficacy of immunotherapy in PRCC.
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Affiliation(s)
- Dongshan Chen
- Department of Urology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
- Department of Urology, Tongji Hospital, Tongji Medical College, Wuhan, China
- Department of Urology, Beijing Chaoyang Hospital Affiliated Capital Medical University, Beijing, China
| | - Chen Zhang
- School of Life Science and Engineering, Handan University, Handan, China
| | - Yuanwei Zang
- Department of Urology, Qilu Hospital of Shandong University, Jinan, China
| | - Wei Wang
- Department of Urology, Beijing Chaoyang Hospital Affiliated Capital Medical University, Beijing, China
| | - Jiandong Zhang
- Department of Urology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
- Department of Urology, Tongji Hospital, Tongji Medical College, Wuhan, China
- Department of Urology, Beijing Chaoyang Hospital Affiliated Capital Medical University, Beijing, China
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15
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Qian S, Chou CA, Li JS. Deep multi-modal learning for joint linear representation of nonlinear dynamical systems. Sci Rep 2022; 12:12807. [PMID: 35896569 PMCID: PMC9329370 DOI: 10.1038/s41598-022-15669-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/28/2022] [Indexed: 11/09/2022] Open
Abstract
Dynamical systems pervasively seen in most real-life applications are complex and behave by following certain evolution rules or dynamical patterns, which are linear, non-linear, or stochastic. The underlying dynamics (or evolution rule) of such complex systems, if found, can be used for understanding the system behavior, and furthermore for system prediction and control. It is common to analyze the system's dynamics through observations in different modality approaches. For instance, to recognize patient deterioration in acute care, it usually relies on monitoring and analyzing vital signs and other observations, such as blood pressure, heart rate, respiration, and electroencephalography. These observations convey the information describing the same target system, but the dynamics is not able to be directly characterized due to high complexity of individual modality and maybe time-delay interactions among modalities. In this work, we suppose that the state behavior of a dynamical system follows an intrinsic dynamics shared among these modalities. We specifically propose a new deep auto-encoder framework using the Koopman operator theory to derive the joint linear dynamics for a target system in a space spanned by the intrinsic coordinates. The proposed method aims to reconstruct the original system states by learning the information provided among multiple modalities. Furthermore, with the derived intrinsic dynamics, our method is capable of restoring the missing observations within and across modalities, and used for predicting the future states of the system that follows the same evolution rule.
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Affiliation(s)
- Shaodi Qian
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02215, USA
| | - Chun-An Chou
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02215, USA.
| | - Jr-Shin Li
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
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16
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Cluster analysis of extracellular matrix biomarkers predicts the development of impaired systolic function within 1 year of acute myocardial infarction. Heart Vessels 2022; 37:2029-2038. [PMID: 35896723 PMCID: PMC9579085 DOI: 10.1007/s00380-022-02118-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 06/15/2022] [Indexed: 11/25/2022]
Abstract
The clinical utility of combining extracellular matrix (ECM) biomarkers to predict the development of impaired systolic function following acute myocardial infarction (AMI) remains largely undetermined. A combination of ELISA and multiplexing assays were performed to measure matrix metalloproteinase (MMP)-2, MMP-3, MMP-8, MMP-9, periostin, N-terminal type I procollagen (PINP) and tissue inhibitor of matrix metalloproteinase-1 (TIMP-1) in plasma samples from 120 AMI patients. All patients had an echocardiogram within 1 year of AMI, and were divided into impaired (n = 37, LVEF < 50%) and preserved (n = 83, LVEF ≥ 50%) systolic function groups. Exploratory factor analysis was performed on log-transformed biomarkers using principle axis analysis with Oblimin rotation. Cluster analysis was performed on log-transformed and normalised biomarkers using Ward’s method of minimum variance and the squared Euclidean distance metric. Upon univariate analysis, current smoking, prescription of ACE inhibitors at discharge, peak hsTnT > 610 ng/L (median), MMP-8 levels, Factor 1 scores and Cluster One assignment were predictive of impaired systolic function. Upon multivariate analysis, Cluster One assignment (odds ratio [95% CI], 2.74 [1.04–7.23], p = 0.04) remained an independent predictor of systolic dysfunction in combination with clinical variables. These observations support the usefulness of combining ECM biomarkers using cluster analysis for predicting the development of impaired systolic function in AMI patients.
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17
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Teixeira M, Pereira T, Silva F, Cunha A, Oliveira HP. Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2037-2040. [PMID: 36086366 DOI: 10.1109/embc48229.2022.9871704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Lung cancer is the leading cause of cancer death worldwide. Early low-dose computed tomography (CT) screening can decrease its mortality rate and computer-aided diagnoses systems may make these screenings more accessible. Radiomic features and supervised machine learning have traditionally been employed in these systems. Contrary to supervised methods, unsupervised learning techniques do not require large amounts of annotated data which are labor-intensive to gather and long training times. Therefore, recent approaches have used unsupervised methods, such as clustering, to improve the performance of supervised models. However, an analysis of purely unsupervised methods for malignancy prediction of lung nodules from CT images has not been performed. This work studies nodule malignancy in the LIDC-IDRI image collection of chest CT scans using established radiomic features and unsupervised learning methods based on k-Means, Spectral Clustering, and Gaussian Mixture clustering. All tested methods resulted in clusters of high homogeneity malignancy. Results suggest convex feature distributions and well-separated feature subspaces associated with different diagnoses. Furthermore, diagnosis uncertainty may be explained by common characteristics captured by radiomic features. The k-Means and Gaussian Mixture models are able to generalize to unseen data, achieving a balanced accuracy of 87.23% and 86.96% when inference was tested. These results motivate the usage of unsupervised approaches for malignancy prediction of lung nodules, such as cluster-then-label models. Clinical Relevance- Unsupervised clustering of radiomic features of lung nodules in chest CT scans can differentiate between malignant and benign cases and reflects experts' diagnosis uncertainty.
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18
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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China.,Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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19
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Ullmann T, Beer A, Hünemörder M, Seidl T, Boulesteix AL. Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. ADV DATA ANAL CLASSI 2022. [DOI: 10.1007/s11634-022-00496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractWhen researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms’ performance with existing competitors. However, such studies are likely to be optimistically biased towards the new algorithms, as the authors have a vested interest in presenting their method as favorably as possible in order to increase their chances of getting published. Therefore, the superior performance of newly introduced cluster algorithms is over-optimistic and might not be confirmed in independent benchmark studies performed by neutral and unbiased authors. This problem is known among many researchers, but so far, the different mechanisms leading to over-optimism in cluster algorithm evaluation have never been systematically studied and discussed. Researchers are thus often not aware of the full extent of the problem. We present an illustrative study to illuminate the mechanisms by which authors—consciously or unconsciously—paint their cluster algorithm’s performance in an over-optimistic light. Using the recently published cluster algorithm Rock as an example, we demonstrate how optimization of the used datasets or data characteristics, of the algorithm’s parameters and of the choice of the competing cluster algorithms leads to Rock’s performance appearing better than it actually is. Our study is thus a cautionary tale that illustrates how easy it can be for researchers to claim apparent “superiority” of a new cluster algorithm. This illuminates the vital importance of strategies for avoiding the problems of over-optimism (such as, e.g., neutral benchmark studies), which we also discuss in the article.
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20
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Bauer GR, Mahendran M, Walwyn C, Shokoohi M. Latent variable and clustering methods in intersectionality research: systematic review of methods applications. Soc Psychiatry Psychiatr Epidemiol 2022; 57:221-237. [PMID: 34773462 PMCID: PMC8784367 DOI: 10.1007/s00127-021-02195-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/31/2021] [Indexed: 12/04/2022]
Abstract
PURPOSE An intersectionality framework has been increasingly incorporated into quantitative study of health inequity, to incorporate social power in meaningful ways. Researchers have identified "person-centered" methods that cluster within-individual characteristics as appropriate to intersectionality. We aimed to review their use and match with theory. METHODS We conducted a multidisciplinary systematic review of English-language quantitative studies wherein authors explicitly stated an intersectional approach, and used clustering methods. We extracted study characteristics and applications of intersectionality. RESULTS 782 studies with quantitative applications of intersectionality were identified, of which 16 were eligible: eight using latent class analysis, two latent profile analysis, and six clustering methods. Papers used cross-sectional data (100.0%) primarily had U.S. lead authors (68.8%) and were published within psychology, social sciences, and health journals. While 87.5% of papers defined intersectionality and 93.8% cited foundational authors, engagement with intersectionality method literature was more limited. Clustering variables were based on social identities/positions (e.g., gender), dimensions of identity (e.g., race centrality), or processes (e.g., stigma). Results most commonly included four classes/clusters (60.0%), which were frequently used in additional analyses. These described sociodemographic differences across classes/clusters, or used classes/clusters as an exposure variable to predict outcomes in regression analysis, structural equation modeling, mediation, or survival analysis. Author rationales for method choice included both theoretical/intersectional and statistical arguments. CONCLUSION Latent variable and clustering methods were used in varied ways in intersectional approaches, and reflected differing matches between theory and methods. We highlight situations in which these methods may be advantageous, and missed opportunities for additional uses.
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Affiliation(s)
- Greta R Bauer
- Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
| | - Mayuri Mahendran
- Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Chantel Walwyn
- Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Mostafa Shokoohi
- Social and Behavioural Health Sciences, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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21
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Ma C, Wu M, Ma S. Analysis of cancer omics data: a selective review of statistical techniques. Brief Bioinform 2022; 23:6510158. [PMID: 35039832 DOI: 10.1093/bib/bbab585] [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: 09/20/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.
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Affiliation(s)
- Chenjin Ma
- College of Statistics and Data Science, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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22
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Payne S, Fuller E, Spirou G, Zhang CQ. Automatic Quasi-Clique Merger Algorithm - a Hierarchical Clustering Based on Subgraph-Density. PHYSICA A 2022; 585:126442. [PMID: 34737487 PMCID: PMC8562650 DOI: 10.1016/j.physa.2021.126442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The Automatic Quasi-clique Merger algorithm is a new algorithm adapted from early work published under the name QCM (introduced by Ou and Zhang in 2007). The AQCM algorithm performs hierarchical clustering in any data set for which there is an associated similarity measure quantifying the similarity of any data i and data j. Importantly, the method exhibits two valuable performance properties: 1) the ability to automatically return either a larger or smaller number of clusters depending on the inherent properties of the data rather than on a parameter. 2) the ability to return a very large number of relatively small clusters automatically when such clusters are reasonably well defined in a data set. In this work we present the general idea of a quasi-clique agglomerative approach, provide the full details of the mathematical steps of the AQCM algorithm, and explain some of the motivation behind the new methodology. The main achievement of the new methodology is that the agglomerative process now unfolds adaptively according to the inherent structure unique to a given data set, and this happens without the time-costly parameter adjustment that drove the previous QCM algorithm. For this reason we call the new algorithm automatic. We provide a demonstration of the algorithm's performance at the task of community detection in a social media network of 22,900 nodes.
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23
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SenGupta A, Roy M. Clustering on the Torus. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021. [DOI: 10.1007/s42519-021-00178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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24
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Kerner J, Dogan A, von Recum H. Machine learning and big data provide crucial insight for future biomaterials discovery and research. Acta Biomater 2021; 130:54-65. [PMID: 34087445 DOI: 10.1016/j.actbio.2021.05.053] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.
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Affiliation(s)
- Jacob Kerner
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Alan Dogan
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Horst von Recum
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
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25
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A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force. Pattern Anal Appl 2021. [DOI: 10.1007/s10044-021-00981-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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26
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A combined biomarker approach for characterising extracellular matrix profiles in acute myocardial infarction. Sci Rep 2021; 11:12705. [PMID: 34135421 PMCID: PMC8209070 DOI: 10.1038/s41598-021-92108-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/26/2021] [Indexed: 11/09/2022] Open
Abstract
Extracellular matrix (ECM) biomarkers are useful for measuring underlying molecular activity associated with cardiac repair following acute myocardial infarction (AMI). The aim of this study was to conduct exploratory factor analysis (EFA) to examine the interrelationships between ECM biomarkers, and cluster analysis to identify if distinct ECM profiles could distinguish patient risk in AMI. Ten ECM biomarkers were measured from plasma in 140 AMI patients: MMP-2, -3, -8, -9, periostin, procollagen I N-Terminal propeptide, osteopontin, TGF-β1, TIMP-1 and -4. EFA grouped eight ECM biomarkers into a two-factor solution, which comprised three biomarkers in Factor 1 and five biomarkers in Factor 2. Notably, ECM biomarkers were not separated based on biological function. Cluster analysis grouped AMI patients into three distinct clusters. Cluster One (n = 54) had increased levels of MMP-8, MMP-9, and TGF-B1. Cluster Two (n = 43) had elevated levels of MMP-2, MMP-3, osteopontin, periostin and TIMP-1, and increased high-sensitivity troponin T and GRACE scores. Cluster Three (n = 43) had decreased levels of ECM biomarkers. Circulating ECM biomarkers demonstrated collinearity and entwined biological functions based on EFA analysis. Using cluster analysis, patients with similar clinical presentations could be separated into distinct ECM profiles that were associated with differential patient risk. Clinical significance remains to be determined.
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27
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Sheikhi G, Altınçay H. A novel dissimilarity metric based on feature‐to‐feature scatter frequencies for clustering‐based feature selection in biomedical data. Comput Intell 2021. [DOI: 10.1111/coin.12470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ghazaal Sheikhi
- Department of Computer Engineering Final International University Kyrenia North Cyprus Turkey
| | - Hakan Altınçay
- Department of Computer Engineering Eastern Mediterranean University Famagusta North Cyprus Turkey
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28
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Karatzas E, Gkonta M, Hotova J, Baltoumas FA, Kontou PI, Bobotsis CJ, Bagos PG, Pavlopoulos GA. VICTOR: A visual analytics web application for comparing cluster sets. Comput Biol Med 2021; 135:104557. [PMID: 34139436 DOI: 10.1016/j.compbiomed.2021.104557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 01/21/2023]
Abstract
Clustering is the process of grouping different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterizations may produce quite dissimilar cluster sets. In this way, the user is often forced to manually filter and compare these results in order to decide which of them generate the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the visual comparison of the results of various clustering algorithms. VICTOR can handle multiple cluster set results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of data tables or interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTOR's functionality using three examples. In the first case, we compare five different network clustering algorithms on a Yeast protein-protein interaction dataset whereas in the second example, we test four different parameters of the MCL clustering algorithm on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction. VICTOR is available at http://victor.pavlopouloslab.info or http://bib.fleming.gr:3838/VICTOR.
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Affiliation(s)
- Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece; Department of Biology, University of Athens, Greece
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece; Department of Biology, University of Athens, Greece
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Panagiota I Kontou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
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Arbet J, Zhuang Y, Litkowski E, Saba L, Kechris K. Comparing Statistical Tests for Differential Network Analysis of Gene Modules. Front Genet 2021; 12:630215. [PMID: 34093641 PMCID: PMC8170128 DOI: 10.3389/fgene.2021.630215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.
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Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Yaxu Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Elizabeth Litkowski
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laura Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora CO, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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Schulze HG, Rangan S, Vardaki MZ, Iworima DG, Kieffer TJ, Blades MW, Turner RFB, Piret JM. Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources. APPLIED SPECTROSCOPY 2021; 75:520-530. [PMID: 33231477 DOI: 10.1177/0003702820979331] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.
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Affiliation(s)
- H Georg Schulze
- Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Shreyas Rangan
- Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Martha Z Vardaki
- Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Diepiriye G Iworima
- Department of Cellular and Physiological Sciences, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Timothy J Kieffer
- Department of Cellular and Physiological Sciences, 8166The University of British Columbia, Vancouver, BC, Canada
- Department of Surgery, 8166The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Michael W Blades
- Department of Chemistry, 8166The University of British Columbia, Vancouver, BC, Canada
| | - Robin F B Turner
- Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, 8166The University of British Columbia, Vancouver, BC, Canada
- Department of Electrical and Computer Engineering, 8166The University of British Columbia, Vancouver, BC, Canada
| | - James M Piret
- Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, 8166The University of British Columbia, Vancouver, BC, Canada
- Department of Chemical and Biological Engineering, 8166The University of British Columbia, Vancouver, BC, Canada
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Yang L, Pijuan-Galito S, Rho HS, Vasilevich AS, Eren AD, Ge L, Habibović P, Alexander MR, de Boer J, Carlier A, van Rijn P, Zhou Q. High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology. Chem Rev 2021; 121:4561-4677. [PMID: 33705116 PMCID: PMC8154331 DOI: 10.1021/acs.chemrev.0c00752] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Indexed: 02/07/2023]
Abstract
The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.
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Affiliation(s)
- Liangliang Yang
- University
of Groningen, W. J. Kolff Institute for Biomedical Engineering and
Materials Science, Department of Biomedical Engineering, University Medical Center Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Sara Pijuan-Galito
- School
of Pharmacy, Biodiscovery Institute, University
of Nottingham, University Park, Nottingham NG7 2RD, U.K.
| | - Hoon Suk Rho
- Department
of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Aliaksei S. Vasilevich
- Department
of Biomedical Engineering, Eindhoven University
of Technology, 5600 MB Eindhoven, The Netherlands
| | - Aysegul Dede Eren
- Department
of Biomedical Engineering, Eindhoven University
of Technology, 5600 MB Eindhoven, The Netherlands
| | - Lu Ge
- University
of Groningen, W. J. Kolff Institute for Biomedical Engineering and
Materials Science, Department of Biomedical Engineering, University Medical Center Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Pamela Habibović
- Department
of Instructive Biomaterials Engineering, MERLN Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Morgan R. Alexander
- School
of Pharmacy, Boots Science Building, University
of Nottingham, University Park, Nottingham NG7 2RD, U.K.
| | - Jan de Boer
- Department
of Biomedical Engineering, Eindhoven University
of Technology, 5600 MB Eindhoven, The Netherlands
| | - Aurélie Carlier
- Department
of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired
Regenerative Medicine, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Patrick van Rijn
- University
of Groningen, W. J. Kolff Institute for Biomedical Engineering and
Materials Science, Department of Biomedical Engineering, University Medical Center Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Qihui Zhou
- Institute
for Translational Medicine, Department of Stomatology, The Affiliated
Hospital of Qingdao University, Qingdao
University, Qingdao 266003, China
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Coombes CE, Liu X, Abrams ZB, Coombes KR, Brock G. Simulation-derived best practices for clustering clinical data. J Biomed Inform 2021; 118:103788. [PMID: 33862229 DOI: 10.1016/j.jbi.2021.103788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 03/23/2021] [Accepted: 04/11/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Clustering analyses in clinical contexts hold promise to improve the understanding of patient phenotype and disease course in chronic and acute clinical medicine. However, work remains to ensure that solutions are rigorous, valid, and reproducible. In this paper, we evaluate best practices for dissimilarity matrix calculation and clustering on mixed-type, clinical data. METHODS We simulate clinical data to represent problems in clinical trials, cohort studies, and EHR data, including single-type datasets (binary, continuous, categorical) and 4 data mixtures. We test 5 single distance metrics (Jaccard, Hamming, Gower, Manhattan, Euclidean) and 3 mixed distance metrics (DAISY, Supersom, and Mercator) with 3 clustering algorithms (hierarchical (HC), k-medoids, self-organizing maps (SOM)). We quantitatively and visually validate by Adjusted Rand Index (ARI) and silhouette width (SW). We applied our best methods to two real-world data sets: (1) 21 features collected on 247 patients with chronic lymphocytic leukemia, and (2) 40 features collected on 6000 patients admitted to an intensive care unit. RESULTS HC outperformed k-medoids and SOM by ARI across data types. DAISY produced the highest mean ARI for mixed data types for all mixtures except unbalanced mixtures dominated by continuous data. Compared to other methods, DAISY with HC uncovered superior, separable clusters in both real-world data sets. DISCUSSION Selecting an appropriate mixed-type metric allows the investigator to obtain optimal separation of patient clusters and get maximum use of their data. Superior metrics for mixed-type data handle multiple data types using multiple, type-focused distances. Better subclassification of disease opens avenues for targeted treatments, precision medicine, clinical decision support, and improved patient outcomes.
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Affiliation(s)
- Caitlin E Coombes
- The Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH 43210, USA.
| | - Xin Liu
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
| | - Zachary B Abrams
- Institute for Informatics, Washington University in St. Louis, 444 Forest Park Ave., St. Louis, MO 63108, USA.
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
| | - Guy Brock
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr, Columbus, OH 43210, USA.
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Tang Y, Li Z, Yang D, Fang Y, Gao S, Liang S, Liu T. Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning. Chin Med 2021; 16:2. [PMID: 33407711 PMCID: PMC7789502 DOI: 10.1186/s13020-020-00409-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022] Open
Abstract
Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
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Affiliation(s)
- Yuqi Tang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Zechen Li
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Dongdong Yang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China.
| | - Yu Fang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shanshan Gao
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shan Liang
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Tao Liu
- Electronic Engineering College, Chengdu University of Information Technology, Chengdu, 610225, China
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Multi-assignment clustering: Machine learning from a biological perspective. J Biotechnol 2020; 326:1-10. [PMID: 33285150 DOI: 10.1016/j.jbiotec.2020.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/03/2020] [Indexed: 11/21/2022]
Abstract
A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).
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Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints. SUSTAINABILITY 2020. [DOI: 10.3390/su122310024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The rapid aging of the population has posed significant challenges to society and raised new demand for transportation services. Understanding travel needs of the elderly is crucial to making effective strategies for accommodating their demand in many newly motorized cities in developing countries such as China. Using a Markov-chain-based mixture model, we identify two main activity patterns of the elderly: recreation-shopping-oriented (RS-oriented) pattern and schooling-drop-off/pick-up-oriented (SDP-oriented) pattern. Elderly people in the RS-oriented pattern enjoy a cozy life with much time spent on recreation and shopping activities, while those in the SDP-oriented pattern take responsibility of sending grandchildren to school and taking them back home. The RS-oriented elderly people are faced with spatial constraints to access the sparsely distributed recreational sights; however, the SDP-oriented group is subject to temporal constraints when making daily trips. These results would encourage policy makers to reconsider the role of transportation in aged people’s lives and better accommodate their demand through designing safer walking and cycling environment and improving the quality of transit services.
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Peng CC, Tsai CJ, Chang TY, Yeh JY, Hua PW. A new approach to generate diversified clusters for small data sets. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Blumenberg L, Ruggles KV. Hypercluster: a flexible tool for parallelized unsupervised clustering optimization. BMC Bioinformatics 2020; 21:428. [PMID: 32993491 PMCID: PMC7525959 DOI: 10.1186/s12859-020-03774-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 09/22/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow. RESULTS We present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model. CONCLUSIONS Hypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster .
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Affiliation(s)
- Lili Blumenberg
- Institute of Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016 USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY 10016 USA
| | - Kelly V. Ruggles
- Institute of Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016 USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY 10016 USA
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Hier DB, Kopel J, Brint SU, Wunsch DC, Olbricht GR, Azizi S, Allen B. Evaluation of standard and semantically-augmented distance metrics for neurology patients. BMC Med Inform Decis Mak 2020; 20:203. [PMID: 32843023 PMCID: PMC7448345 DOI: 10.1186/s12911-020-01217-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 08/12/2020] [Indexed: 12/23/2022] Open
Abstract
Background Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks. Methods We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics. Results Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric. Conclusion Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances.
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Affiliation(s)
- Daniel B Hier
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, 60612, USA.
| | - Jonathan Kopel
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Steven U Brint
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Donald C Wunsch
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, 65401, USA
| | - Gayla R Olbricht
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, 65401, USA
| | - Sima Azizi
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, 65401, USA
| | - Blaine Allen
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, 65401, USA
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Koskela HO, Selander TA, Lätti AM. Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life. Respir Res 2020; 21:219. [PMID: 32819357 PMCID: PMC7441640 DOI: 10.1186/s12931-020-01485-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/11/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Recognition of disorder phenotypes may help to estimate prognosis and to guide the clinical management. Current cough management guidelines classify patients according to the duration of the cough episode. However, this classification is not based on phenotype analyses. The present study aimed to identify cough phenotypes by clustering. METHODS An email survey among employed, working-age subjects identified 975 patients with current cough. All filled in a comprehensive 80-item questionnaire including the Leicester Cough Questionnaire. Phenotypes were identified utilizing K-means partitional clustering. A subgroup filled in a follow-up questionnaire 12 months later to investigate the possible differences in the prognosis between the phenotypes. RESULTS Two clusters were found. The cluster A included 608 patients (62.4% of the population) and the cluster B 367 patients (37.6%). The three most important variables to separate the clusters were the number of the triggers of cough (mean 2.63 (SD 2.22) vs. 6.95 (2.30), respectively, p < 0.001), the number of the cough background disorders (chronic rhinosinusitis, current asthma, gastroesophageal reflux disease, 0.29 (0.50) vs. 1.28 (0.75), respectively, p < 0.001), and the Leicester Cough Questionnaire physical domain (5.33 (0.76) vs. 4.25 (0.84), respectively, p < 0.001). There were significant interrelationships between these three variables (each p < 0.001). Duration of the episode was not among the most important variables to separate the clusters. At 12 months, 27.0% of the patients of the cluster A and 46.1% of the patients of the cluster B suffered from cough that had continued without interruptions from the first survey (p < 0.001). CONCLUSIONS Two cough phenotypes could be identified. Cluster A represents phenotype A, which includes the majority of patients and has a tendency to heal by itself. The authors propose that cluster B represents phenotype TBQ (Triggers, Background disorders, Quality of life impairment). Given the poor prognosis of this phenotype, it urges a prompt and comprehensive clinical evaluation regardless of the duration of the cough episode.
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Affiliation(s)
- Heikki O Koskela
- Unit for Medicine and Clinical Research, Pulmonary Division, Kuopio University Hospital, Postal address: PL 100, 70029 KYS, Kuopio, Finland.
- School of Medicine, University of Eastern Finland, Kuopio, Finland.
| | | | - Anne M Lätti
- Unit for Medicine and Clinical Research, Pulmonary Division, Kuopio University Hospital, Postal address: PL 100, 70029 KYS, Kuopio, Finland
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Prajapati A, Parashar A, Chhabra JK. Restructuring Object-Oriented Software Systems Using Various Aspects of Class Information. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04785-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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41
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Sun Y. Potential quality improvement of stochastic optical localization nanoscopy images obtained by frame by frame localization algorithms. Sci Rep 2020; 10:11844. [PMID: 32678167 PMCID: PMC7367355 DOI: 10.1038/s41598-020-68564-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/26/2020] [Indexed: 12/28/2022] Open
Abstract
A data movie of stochastic optical localization nanoscopy contains spatial and temporal correlations, both providing information of emitter locations. The majority of localization algorithms in the literature estimate emitter locations by frame-by-frame localization (FFL), which exploit only the spatial correlation and leave the temporal correlation into the FFL nanoscopy images. The temporal correlation contained in the FFL images, if exploited, can improve the localization accuracy and the image quality. In this paper, we analyze the properties of the FFL images in terms of root mean square minimum distance (RMSMD) and root mean square error (RMSE). It is shown that RMSMD and RMSE can be potentially reduced by a maximum fold equal to the square root of the average number of activations per emitter. Analyzed and revealed are also several statistical properties of RMSMD and RMSE and their relationship with respect to a large number of data frames, bias and variance of localization errors, small localization errors, sample drift, and the worst FFL image. Numerical examples are taken and the results confirm the prediction of analysis. The ideas about how to develop an algorithm to exploit the temporal correlation of FFL images are also briefly discussed. The results suggest development of two kinds of localization algorithms: the algorithms that can exploit the temporal correlation of FFL images and the unbiased localization algorithms.
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Affiliation(s)
- Yi Sun
- Electrical Engineering Department, Nanoscopy Laboratory, The City College of City University of New York, New York, NY, 10031, USA.
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Wambogo EA, Vaudin AM, Moshfegh AJ, Spungen JH, Doren JMVAN, Sahyoun NR. Toward a Better Understanding of Listeriosis Risk among Older Adults in the United States: Characterizing Dietary Patterns and the Sociodemographic and Economic Attributes of Consumers with These Patterns. J Food Prot 2020; 83:1208-1217. [PMID: 32221521 DOI: 10.4315/jfp-19-617] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 03/12/2020] [Indexed: 12/26/2022]
Abstract
ABSTRACT Older adults are at higher risk of invasive listeriosis compared with the general population. Some foods are more likely than others to be contaminated with or to contain high levels of Listeria monocytogenes. The objectives of this study were to (i) determine dietary consumption patterns among older adults in the United States; (ii) evaluate sociodemographic and economic characteristics of older adults associated with each pattern; (iii) determine intake of foods associated with larger relative risk of listeriosis within these patterns; and (iv) rank these patterns based on risk. Data related to older adults (age 60 and older) participating in the cross-sectional National Health and Nutrition Examination Surveys (NHANES) 2009 to 2010, 2011 to 2012, and 2013 to 2014 (n = 4,967) were included in these analyses. Cluster analysis was used to define dietary patterns based on 24-h dietary recalls from day 1 and day 2. Mean intake of foods associated with higher risk of listeriosis was examined within each pattern, and analysis of variance with Dunnett's method of adjustment was used to evaluate significant differences in mean intake of foods. Patterns were ranked based on relative risk of listeriosis, using outbreak illness attribution data. Five distinct dietary patterns were identified. Patterns ranked at highest relative risk of listeriosis, based on U.S. outbreak illness attribution data, were characterized by relatively higher intakes of fruits, vegetables, and cheeses (∼13% respondents) or cereal, milk, and yogurt (∼14% respondents). Individuals consuming these dietary patterns differed in sex, race, food security, self-rated diet quality, and self-rated health. Cluster analysis, despite methodological limitations, provides new information on consumption, sociodemographic, and economic characteristics of subgroups within susceptible populations, which may be used to target educational messages. HIGHLIGHTS
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Affiliation(s)
- Edwina A Wambogo
- Department of Nutrition and Food Science, 0102 Skinner Building, University of Maryland, College Park, Maryland 20742 (ORCID: https://orcid.org/0000-0002-9738-3452 [N.R.S.])
| | - Anna M Vaudin
- Department of Nutrition and Food Science, 0102 Skinner Building, University of Maryland, College Park, Maryland 20742 (ORCID: https://orcid.org/0000-0002-9738-3452 [N.R.S.])
| | - Alanna J Moshfegh
- U.S. Department of Agriculture, Agricultural Research Service, Food Surveys Research Group, Beltsville Human Nutrition Research Center, 10300 Baltimore Avenue, Building 005, Room 102, BARC-West, Beltsville, Maryland 20705
| | - Judith H Spungen
- Center for Food Safety and Applied Nutrition, Division of Risk Assessment and Decision Analysis, Office of Analytics and Outreach, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740, USA
| | - Jane M VAN Doren
- Center for Food Safety and Applied Nutrition, Division of Risk Assessment and Decision Analysis, Office of Analytics and Outreach, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740, USA
| | - Nadine R Sahyoun
- Department of Nutrition and Food Science, 0102 Skinner Building, University of Maryland, College Park, Maryland 20742 (ORCID: https://orcid.org/0000-0002-9738-3452 [N.R.S.])
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43
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Coombes CE, Abrams ZB, Li S, Abruzzo LV, Coombes KR. Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. J Am Med Inform Assoc 2020; 27:1019-1027. [PMID: 32483590 PMCID: PMC7647286 DOI: 10.1093/jamia/ocaa060] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/08/2020] [Accepted: 04/24/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that clustering could discover prognostic groups from patients with chronic lymphocytic leukemia, a disease that provides biological validation through well-understood outcomes. METHODS To address this challenge, we applied k-medoids clustering with 10 distance metrics to 2 experiments ("A" and "B") with mixed clinical features collapsed to binary vectors and visualized with both multidimensional scaling and t-stochastic neighbor embedding. To assess prognostic utility, we performed survival analysis using a Cox proportional hazard model, log-rank test, and Kaplan-Meier curves. RESULTS In both experiments, survival analysis revealed a statistically significant association between clusters and survival outcomes (A: overall survival, P = .0164; B: time from diagnosis to treatment, P = .0039). Multidimensional scaling separated clusters along a gradient mirroring the order of overall survival. Longer survival was associated with mutated immunoglobulin heavy-chain variable region gene (IGHV) status, absent Zap 70 expression, female sex, and younger age. CONCLUSIONS This approach to mixed-type data handling and selection of distance metric captured well-understood, binary, prognostic markers in chronic lymphocytic leukemia (sex, IGHV mutation status, ZAP70 expression status) with high fidelity.
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MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Female
- Humans
- Immunoglobulin Heavy Chains/genetics
- Kaplan-Meier Estimate
- Leukemia, Lymphocytic, Chronic, B-Cell/immunology
- Leukemia, Lymphocytic, Chronic, B-Cell/metabolism
- Leukemia, Lymphocytic, Chronic, B-Cell/mortality
- Male
- Middle Aged
- Mutation
- Prognosis
- Proportional Hazards Models
- Unsupervised Machine Learning
- ZAP-70 Protein-Tyrosine Kinase/metabolism
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Affiliation(s)
- Caitlin E Coombes
- The Ohio State University College of Medicine, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
| | - Zachary B Abrams
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
| | - Suli Li
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Lynne V Abruzzo
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
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44
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Wildeboer RR, van Sloun RJG, Wijkstra H, Mischi M. Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105316. [PMID: 31951873 DOI: 10.1016/j.cmpb.2020.105316] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/09/2019] [Accepted: 01/04/2020] [Indexed: 05/16/2023]
Abstract
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.
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Affiliation(s)
- Rogier R Wildeboer
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Ruud J G van Sloun
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Hessel Wijkstra
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands; Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Massimo Mischi
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands
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45
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Vermeulen S, de Boer J. Screening as a strategy to drive regenerative medicine research. Methods 2020; 190:80-95. [PMID: 32278807 DOI: 10.1016/j.ymeth.2020.04.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 03/30/2020] [Accepted: 04/06/2020] [Indexed: 02/07/2023] Open
Abstract
In the field of regenerative medicine, optimization of the parameters leading to a desirable outcome remains a huge challenge. Examples include protocols for the guided differentiation of pluripotent cells towards specialized and functional cell types, phenotypic maintenance of primary cells in cell culture, or engineering of materials for improved tissue interaction with medical implants. This challenge originates from the enormous design space for biomaterials, chemical and biochemical compounds, and incomplete knowledge of the guiding biological principles. To tackle this challenge, high-throughput platforms allow screening of multiple perturbations in one experimental setup. In this review, we provide an overview of screening platforms that are used in regenerative medicine. We discuss their fabrication techniques, and in silico tools to analyze the extensive data sets typically generated by these platforms.
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Affiliation(s)
- Steven Vermeulen
- Laboratory for Cell Biology-Inspired Tissue Engineering, MERLN Institute, University of Maastricht, Maastricht, the Netherlands; BioInterface Science Group, Department of Biomedical Engineering and Institute for Complex Molecular Systems, University of Eindhoven, Eindhoven, the Netherlands
| | - Jan de Boer
- BioInterface Science Group, Department of Biomedical Engineering and Institute for Complex Molecular Systems, University of Eindhoven, Eindhoven, the Netherlands.
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46
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He H, Tan Y. Unsupervised Classification of Multivariate Time Series Using VPCA and Fuzzy Clustering With Spatial Weighted Matrix Distance. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:1096-1105. [PMID: 30561360 DOI: 10.1109/tcyb.2018.2883388] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Due to high dimensionality and multiple variables, unsupervised classification of multivariate time series (MTS) involves more challenging problems than those of univariate ones. Unlike the vectorization of a feature matrix in traditional clustering algorithms, an unsupervised pattern recognition scheme based on matrix data is proposed for MTS samples in this paper. To reduce the computational load and time consumption, a novel variable-based principal component analysis (VPCA) is first devised for the dimensionality reduction of MTS samples. Afterward, a spatial weighted matrix distance-based fuzzy clustering (SWMDFC) algorithm is proposed to directly group MTS samples into clusters as well as preserve the structure of the data matrix. The spatial weighted matrix distance (SWMD) integrates the spatial dimensionality difference of elements of data into the distance of MST pairs. In terms of the SWMD, the MTS samples are clustered without vectorization in the dimensionality-reduced feature matrix space. Finally, three open-access datasets are utilized for the validation of the proposed unsupervised classification scheme. The results show that the VPCA can capture more features of MTS data than principal component analysis (PCA) and 2-D PCA. Furthermore, the clustering performance of SWMDFC is superior to that of fuzzy c -means clustering algorithms based on the Euclidean distance or image Euclidean distance.
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47
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Zhu C, Na N, Sheng H, Feng B, Wang H, Zhu P, Zhang W, Zhang M, Deng Z. Ginkgolic acid inhibits the growth of renal cell carcinoma cells via inactivation of the EGFR signaling pathway. Exp Ther Med 2020; 19:2949-2956. [PMID: 32256780 PMCID: PMC7086188 DOI: 10.3892/etm.2020.8570] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 01/09/2020] [Indexed: 12/15/2022] Open
Abstract
Renal cell carcinoma (RCC) is one of the most common urological malignancies occurring in adult human kidneys worldwide. Recent research on antitumor drugs has focused on plant extracts, a class of compounds that play critical roles in cancer treatment. The present study aimed to investigate the potential antitumor effect of ginkgolic acid (GA) in RCC. Transwell invasion assay, cell counting kit-8 assay and flow cytometry were used to measure cell migration, cell viability and apoptosis, respectively. A network pharmacology approach was applied to identify pathway information, combining molecular docking techniques to screen for key target information. In the present study, GA inhibited the viability and proliferation of RCC cells (786-O and A498), both in vitro and in vivo, via G1 arrest. GA also reduced RCC cell invasion and migration. In addition, the epidermal growth factor receptor (EGFR) was identified as a critical target protein of GA, which significantly inactivated EGFR signaling in RCC (P<0.05). Collectively, the present study provided evidence that GA exerts its anticancer function by directly targeting the EGFR signaling pathway, revealing the potential of GA therapy for RCC.
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Affiliation(s)
- Chao Zhu
- Department of Nephrology, Changhai Hospital, Second Military Medical University, Shanghai 200433, P.R. China
| | - Na Na
- Department of Outpatients, 900th Hospital of The Joint Logistics Support Force (People's Liberation Army), Fuzhou, Fujian 350000, P.R. China
| | - Haibo Sheng
- Department of Urology, Airforce Medical Center (People's Liberation Army), Beijing 100142, P.R. China
| | - Bing Feng
- Department of Nephrology, Changhai Hospital, Second Military Medical University, Shanghai 200433, P.R. China
| | - Hao Wang
- Department of Nephrology, Changhai Hospital, Second Military Medical University, Shanghai 200433, P.R. China
| | - Ping Zhu
- Department of Pathology, 971st Navy Hospital of PLA, Qingdao, Shandong 266071, P.R. China
| | - Wei Zhang
- Department of Pathology, 971st Navy Hospital of PLA, Qingdao, Shandong 266071, P.R. China
| | - Meina Zhang
- Department of Pathology, 971st Navy Hospital of PLA, Qingdao, Shandong 266071, P.R. China
| | - Zhen Deng
- Department of Urology, 900th Hospital of The Joint Logistics Support Force (People's Liberation Army), Fuzhou, Fujian 350000, P.R. China
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48
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Zhao L, Lee VHF, Ng MK, Yan H, Bijlsma MF. Molecular subtyping of cancer: current status and moving toward clinical applications. Brief Bioinform 2020; 20:572-584. [PMID: 29659698 DOI: 10.1093/bib/bby026] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 03/01/2018] [Indexed: 12/14/2022] Open
Abstract
Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.
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Affiliation(s)
- Lan Zhao
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Victor H F Lee
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Michael K Ng
- Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Maarten F Bijlsma
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Academic Medical Center, Amsterdam, The Netherlands
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49
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Bioinformatics Analysis to Screen the Key Prognostic Genes in Tumor Microenvironment of Bladder Cancer. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6034670. [PMID: 32149116 PMCID: PMC7048919 DOI: 10.1155/2020/6034670] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 01/16/2020] [Indexed: 12/19/2022]
Abstract
Bladder cancer (BLCA) is the fifth most common cancer and has the features of low survival rate and high morbidity and mortality. The Cancer Genome Atlas (TCGA) is a pool of global gene expression profile and contains huge amounts of cancer genomics data, which makes it possible to inquire the relationship between gene expression and prognosis of a series of malignant tumors including BLCA. Immune and stromal cells are two major components of tumor microenvironment (TME) which play an important role in judging the prognosis of tumor and influencing the progression of malignant, inflammatory, and metabolic disorders. In our study, we conducted a quantitative analysis of immune and stromal elements based on the ESTIMATE algorithm and thus divided BLCA cases into high and low groups. Then the differentially expressed genes closely related to tumor prognosis between groups were identified and had been shown to correlate with immune response and stromal alterations, which was further confirmed by functional enrichment analysis and protein-protein interaction networks. We validated those genes through BLCA dates downloaded from ArrayExpress and thus got the marker genes to predict prognosis of BLCA. Additionally, immune cell infiltration analysis explored the correlation between the verified genes and immune cells. In conclusion, we identified a series of TME-related genes that assess the prognosis and explored the interaction between TME and tumor prognosis to guide clinical individualized treatment.
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50
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Winder Z, Sudduth TL, Fardo D, Cheng Q, Goldstein LB, Nelson PT, Schmitt FA, Jicha GA, Wilcock DM. Hierarchical Clustering Analyses of Plasma Proteins in Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based on Differential Levels of Angiogenic and Inflammatory Biomarkers. Front Neurosci 2020; 14:84. [PMID: 32116527 PMCID: PMC7016016 DOI: 10.3389/fnins.2020.00084] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 01/21/2020] [Indexed: 12/29/2022] Open
Abstract
Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to identify informative subsets of individuals. Study subjects were diagnosed with mild cognitive impairment due to cerebrovascular disease (MCI-CVD). Through comparison of the two HCA techniques, we were able to identify subsets of individuals, based on differences in VEGF (p < 0.001), MMP1 (p < 0.001), and IL8 (p < 0.001) levels. These profiles provide novel insights into angiogenic and inflammatory pathologies that may contribute to VCID.
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Affiliation(s)
- Zachary Winder
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Physiology, University of Kentucky, Lexington, KY, United States
| | - Tiffany L Sudduth
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States
| | - David Fardo
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Biostatistics, University of Kentucky, Lexington, KY, United States
| | - Qiang Cheng
- Department of Computer Science, University of Kentucky, Lexington, KY, United States
| | - Larry B Goldstein
- Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Peter T Nelson
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Pathology, University of Kentucky, Lexington, KY, United States
| | - Frederick A Schmitt
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Gregory A Jicha
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Donna M Wilcock
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States.,Department of Physiology, University of Kentucky, Lexington, KY, United States
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