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Varsou DD, Kolokathis PD, Antoniou M, Sidiropoulos NK, Tsoumanis A, Papadiamantis AG, Melagraki G, Lynch I, Afantitis A. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation. Comput Struct Biotechnol J 2024; 25:47-60. [PMID: 38646468 PMCID: PMC11026727 DOI: 10.1016/j.csbj.2024.03.020] [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: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
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Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
| | | | | | | | - Andreas Tsoumanis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| | - Anastasios G. Papadiamantis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Antreas Afantitis
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
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2
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Jiao Y, Ji F, Hou L, Lv Y, Zhang J. Lactylation-related gene signature for prognostic prediction and immune infiltration analysis in breast cancer. Heliyon 2024; 10:e24777. [PMID: 38318076 PMCID: PMC10838739 DOI: 10.1016/j.heliyon.2024.e24777] [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: 06/28/2023] [Revised: 01/07/2024] [Accepted: 01/15/2024] [Indexed: 02/07/2024] Open
Abstract
Background Lactylation is implicated in various aspects of tumor biology, but its relation to breast cancer remains poorly understood. This study aimed to explore the roles of the lactylation-related genes in breast cancer and its association with the tumor microenvironment. Methods The expression and mutation patterns of lactylation-related genes were analyzed using the breast cancer data from The Cancer Genome Atlas (TCGA) database and GSE20685 datasets. Unsupervised clustering was used to identify two lactylation clusters. A lactylation-related gene signature was developed and validated using the training and validation cohorts. Immune cell infiltration and drug response were assessed. Results We analyzed the mRNA expression, copy number variations, somatic mutations, and correlation networks of 22 lactylation-related genes in breast cancer tissues. We identified two distinct lactylation clusters with different survival outcomes and immune microenvironments. We further classified the patients into two gene subtypes based on lactylation clusters and identified a 7-gene signature for breast cancer survival prognosis. The prognostic score based on this signature demonstrated prognostic value and predicted the therapeutic response. Conclusion Lactylation-related genes play a critical role in breast cancer by influencing tumor growth, immune microenvironment, and drug response. This lactylation-related gene signature may serve as a prognostic marker and a potential therapeutic target for breast cancer.
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Affiliation(s)
- Yangchi Jiao
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, Shaanxi, China
| | - Fuqing Ji
- Department of Thyroid Breast Surgery, Xi'an NO.3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, Shaanxi, China
| | - Lan Hou
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, Shaanxi, China
| | - Yonggang Lv
- Department of Thyroid Breast Surgery, Xi'an NO.3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, Shaanxi, China
| | - Juliang Zhang
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Air Force Military Medical University, Xi'an, Shaanxi, China
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3
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James EA, Holers VM, Iyer R, Prideaux EB, Rao NL, Rims C, Muir VS, Posso SE, Bloom MS, Zia A, Elliott SE, Adamska JZ, Ai R, Brewer RC, Seifert JA, Moss L, Barzideh S, Demoruelle MK, Striebich CC, Okamoto Y, Sainbayar E, Crook AA, Peterson RA, Vanderlinden LA, Wang W, Boyle DL, Robinson WH, Buckner JH, Firestein GS, Deane KD. Multifaceted immune dysregulation characterizes individuals at-risk for rheumatoid arthritis. Nat Commun 2023; 14:7637. [PMID: 37993439 PMCID: PMC10665556 DOI: 10.1038/s41467-023-43091-8] [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: 02/11/2022] [Accepted: 10/30/2023] [Indexed: 11/24/2023] Open
Abstract
Molecular markers of autoimmunity, such as antibodies to citrullinated protein antigens (ACPA), are detectable prior to inflammatory arthritis (IA) in rheumatoid arthritis (RA) and may define a state that is 'at-risk' for future RA. Here we present a cross-sectional comparative analysis among three groups that include ACPA positive individuals without IA (At-Risk), ACPA negative individuals and individuals with early, ACPA positive clinical RA (Early RA). Differential methylation analysis among the groups identifies non-specific dysregulation in peripheral B, memory and naïve T cells in At-Risk participants, with more specific immunological pathway abnormalities in Early RA. Tetramer studies show increased abundance of T cells recognizing citrullinated (cit) epitopes in At-Risk participants, including expansion of T cells reactive to citrullinated cartilage intermediate layer protein I (cit-CILP); these T cells have Th1, Th17, and T stem cell memory-like phenotypes. Antibody-antigen array analyses show that antibodies targeting cit-clusterin, cit-fibrinogen and cit-histone H4 are elevated in At-Risk and Early RA participants, with the highest levels of antibodies detected in those with Early RA. These findings indicate that an ACPA positive at-risk state is associated with multifaceted immune dysregulation that may represent a potential opportunity for targeted intervention.
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Affiliation(s)
- Eddie A James
- Benaroya Research Institute, Seattle, WA, 98101, USA
| | - V Michael Holers
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
| | - Radhika Iyer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - E Barton Prideaux
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Navin L Rao
- Janssen Research and Development, Spring House, PA, 19477, USA
| | - Cliff Rims
- Benaroya Research Institute, Seattle, WA, 98101, USA
| | | | | | - Michelle S Bloom
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - Amin Zia
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - Serra E Elliott
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - Julia Z Adamska
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - Rizi Ai
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093, USA
| | - R Camille Brewer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | - Jennifer A Seifert
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - LauraKay Moss
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Saman Barzideh
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - M Kristen Demoruelle
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Christopher C Striebich
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Yuko Okamoto
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Division of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Enkhtsogt Sainbayar
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Alexandra A Crook
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Ryan A Peterson
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Lauren A Vanderlinden
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, 92093, USA
| | - David L Boyle
- Division of Rheumatology, Allergy and Immunology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - William H Robinson
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94304, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
| | | | - Gary S Firestein
- Division of Rheumatology, Allergy and Immunology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Kevin D Deane
- Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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5
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Chen Z, Liu X, Zhu Z, Chen J, Wang C, Chen X, Zhu S, Zhang A. A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in clear cell renal cell carcinoma. Front Genet 2022; 13:1039465. [PMID: 36338978 PMCID: PMC9627172 DOI: 10.3389/fgene.2022.1039465] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/10/2022] [Indexed: 09/05/2023] Open
Abstract
Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.
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Affiliation(s)
- Zhuo Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiao Liu
- Shaoxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Shaoxing, Zhejiang, China
| | - Zhengjie Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jinchao Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Chen Wang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xi Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Shaoxing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Aiqin Zhang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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6
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Cho C, Lee E, Park G, Cho E, Kim N, Shin J, Woo S, Ha J, Hwang J. Evaluation of facial skin age based on biophysical properties in vivo. J Cosmet Dermatol 2021; 21:3546-3554. [PMID: 34859944 DOI: 10.1111/jocd.14653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The evaluation of skin age, reflecting overall facial characteristics, has not been established. Previous studies focused on visual assessment or individual-specific feature such as wrinkles or skin color. We studied the evaluation model of skin age index (SAI) including the overall aging features including wrinkles, skin color, pigmentation, elasticity, and hydration. METHODS Total 300 healthy women aged between 20 and 69 years included in this study. Pearson correlation analysis performed to identify the key factors among the biophysical properties with aging and developed the prediction model of SAI. Statistical regression analysis and machine learning technique applied to build the prediction model using the coefficient of determination (R2 ) and root mean square error (RMSE). Validation study of the SAI model performed on 24 women for 6 weeks application with anti-aging product. RESULTS Prediction model of SAI consisted of skin elasticity, wrinkles, skin color (brightness, Pigmented spot, and Uv spot), and hydration, which are major features for aging. The cforest model to assess a SAI using machine learning identified the highest R2 and lowest RMSE compared to other models, such as svmRadial, gaussprRadial, blackboost, rpart, and statistical regression formula. The cforest prediction model confirmed a significant decrease of predicted SAI after 6 weeks of application of anti-aging product. CONCLUSION We developed a prediction model to evaluate a SAI using machine learning, and led to accurate predicted age for overall clinical aging. This model can a good standard index for evaluating facial skin aging and anti-aging products.
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Affiliation(s)
- Changhui Cho
- Department of Genetic Engineering, College of Life Sciences, Graduate School of Biotechnology, Kyung Hee University, Yongin, Korea
| | - Eunyoung Lee
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Gyeonghun Park
- Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | - Eunbyul Cho
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Nahee Kim
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Juhee Shin
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Sanga Woo
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Jaehyoun Ha
- Skin Research Center, Institut d'Expertise Clinique (IEC) KOREA, Suwon, Korea
| | - Jaesung Hwang
- Department of Genetic Engineering, College of Life Sciences, Graduate School of Biotechnology, Kyung Hee University, Yongin, Korea
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7
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Di Stefano AL, Picca A, Saragoussi E, Bielle F, Ducray F, Villa C, Eoli M, Paterra R, Bellu L, Mathon B, Capelle L, Bourg V, Gloaguen A, Philippe C, Frouin V, Schmitt Y, Lerond J, Leclerc J, Lasorella A, Iavarone A, Mokhtari K, Savatovsky J, Alentorn A, Sanson M. Clinical, molecular, and radiomic profile of gliomas with FGFR3-TACC3 fusions. Neuro Oncol 2021; 22:1614-1624. [PMID: 32413119 DOI: 10.1093/neuonc/noaa121] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Actionable fibroblast growth factor receptor 3 (FGFR3)-transforming acidic coiled-coil protein 3 fusions (F3T3) are found in approximately 3% of gliomas, but their characteristics and prognostic significance are still poorly defined. Our goal was to characterize the clinical, radiological, and molecular profile of F3T3 positive diffuse gliomas. METHODS We screened F3T3 fusion by real-time (RT)-PCR and FGFR3 immunohistochemistry in a large series of gliomas, characterized for main genetic alterations, histology, and clinical evolution. We performed a radiological and radiomic case control study, using an exploratory and a validation cohort. RESULTS We screened 1162 diffuse gliomas (951 unselected cases and 211 preselected for FGFR3 protein immunopositivity), identifying 80 F3T3 positive gliomas. F3T3 was mutually exclusive with IDH mutation (P < 0.001) and EGFR amplification (P = 0.01), defining a distinct molecular cluster associated with CDK4 (P = 0.04) and MDM2 amplification (P = 0.03). F3T3 fusion was associated with longer survival for the whole series and for glioblastomas (median overall survival was 31.1 vs 19.9 mo, P = 0.02) and was an independent predictor of better outcome on multivariate analysis.F3T3 positive gliomas had specific MRI features, affecting preferentially insula and temporal lobe, and with poorly defined tumor margins. F3T3 fusion was correctly predicted by radiomics analysis on both the exploratory (area under the curve [AUC] = 0.87) and the validation MRI (AUC = 0.75) cohort. Using Cox proportional hazards models, radiomics predicted survival with a high C-index (0.75, SD 0.04), while the model combining clinical, genetic, and radiomic data showed the highest C-index (0.81, SD 0.04). CONCLUSION F3T3 positive gliomas have distinct molecular and radiological features, and better outcome.
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Affiliation(s)
- Anna Luisa Di Stefano
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée).,Department of Neuropathology 2, Pitié-Salpêtrière Hospital,Paris, France.,Department of Neurology, Foch Hospital, Suresnes, France
| | - Alberto Picca
- C. Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Edouard Saragoussi
- Department of Radiology, Adolphe de Rothschild Ophthalmological Foundation, Paris, France
| | - Franck Bielle
- Department of Neuropathology, Pitié Salpêtrière-Charles Foix, Paris, France
| | - Francois Ducray
- Department of Neuro-Oncology, Civil Hospice of Lyon, University Claude Bernard Lyon 1, Department of Cancer Cell Plasticity, Cancer Research Center of Lyon, Lyon, France.,POLA Network
| | - Chiara Villa
- Department of Pathology, Foch Hospital, Suresnes, France
| | - Marica Eoli
- Unit of Molecular Neuro-Oncology, Carlo Besta Neurological Institute, Milan, Italy
| | - Rosina Paterra
- Unit of Molecular Neuro-Oncology, Carlo Besta Neurological Institute, Milan, Italy
| | - Luisa Bellu
- Department of Neuropathology 2, Pitié-Salpêtrière Hospital,Paris, France
| | - Bertrand Mathon
- Department of Neurosurgery, Pitié-Salpêtrière Hospital, Paris, France
| | - Laurent Capelle
- Department of Neurosurgery, Pitié-Salpêtrière Hospital, Paris, France
| | - Véronique Bourg
- Department of Neurology, Pasteur 2 Hospital, Nice Côte D'Azur University, Nice, France
| | - Arnaud Gloaguen
- Signals and Systems Laboratory, Paris-Saclay University, Gif-sur-Yvette, France.,Neurospin, French Atomic Energy Commission, Paris-Saclay University, Gif-sur-Yvette, France
| | - Cathy Philippe
- Neurospin, French Atomic Energy Commission, Paris-Saclay University, Gif-sur-Yvette, France
| | - Vincent Frouin
- Neurospin, French Atomic Energy Commission, Paris-Saclay University, Gif-sur-Yvette, France
| | - Yohann Schmitt
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée)
| | - Julie Lerond
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée).,Department of Neuropathology, Pitié Salpêtrière-Charles Foix, Paris, France
| | - Julie Leclerc
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée).,Department of Neuropathology, Pitié Salpêtrière-Charles Foix, Paris, France
| | - Anna Lasorella
- Institute for Cancer Genetics, Columbia University, New York, New York, USA.,Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Department of Pediatrics, Columbia University, New York, New York, USA
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University, New York, New York, USA.,Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Department of Neurology, Columbia University, New York, New York, USA
| | - Karima Mokhtari
- Department of Neuropathology, Pitié Salpêtrière-Charles Foix, Paris, France
| | - Julien Savatovsky
- Department of Radiology, Adolphe de Rothschild Ophthalmological Foundation, Paris, France
| | - Agusti Alentorn
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée).,Department of Neuropathology 2, Pitié-Salpêtrière Hospital,Paris, France
| | - Marc Sanson
- Inserm Unit 1127, Sorbonne University, Institute of the Brain and Spinal Cord, Paris, France.,SiRIC CURAMUS, LNCC (équipe labellisée).,Department of Neuropathology 2, Pitié-Salpêtrière Hospital,Paris, France.,OncoNeuroTek, Institute of the Brain and Spinal Cord, Paris, France
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8
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Carballal A, Fernandez-Lozano C, Rodriguez-Fernandez N, Santos I, Romero J. Comparison of Outlier-Tolerant Models for Measuring Visual Complexity. ENTROPY 2020; 22:e22040488. [PMID: 33286263 PMCID: PMC7516971 DOI: 10.3390/e22040488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/18/2020] [Accepted: 04/23/2020] [Indexed: 11/18/2022]
Abstract
Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.
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Affiliation(s)
- Adrian Carballal
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; (C.F.-L.); (N.R.-F.); (I.S.); (J.R.)
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain
- Correspondence:
| | - Carlos Fernandez-Lozano
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; (C.F.-L.); (N.R.-F.); (I.S.); (J.R.)
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain
| | - Nereida Rodriguez-Fernandez
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; (C.F.-L.); (N.R.-F.); (I.S.); (J.R.)
- Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain
| | - Iria Santos
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; (C.F.-L.); (N.R.-F.); (I.S.); (J.R.)
- Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain
| | - Juan Romero
- CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; (C.F.-L.); (N.R.-F.); (I.S.); (J.R.)
- Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain
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9
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Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LJA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, Serra A, Kinaret PAS, Saarimäki LA, Grafström R, Kohonen P, Nymark P, Willighagen E, Puzyn T, Rybinska-Fryca A, Lyubartsev A, Alstrup Jensen K, Brandenburg JG, Lofts S, Svendsen C, Harrison S, Maier D, Tamm K, Jänes J, Sikk L, Dusinska M, Longhin E, Rundén-Pran E, Mariussen E, El Yamani N, Unger W, Radnik J, Tropsha A, Cohen Y, Leszczynski J, Ogilvie Hendren C, Wiesner M, Winkler D, Suzuki N, Yoon TH, Choi JS, Sanabria N, Gulumian M, Lynch I. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput Struct Biotechnol J 2020; 18:583-602. [PMID: 32226594 PMCID: PMC7090366 DOI: 10.1016/j.csbj.2020.02.023] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/18/2020] [Accepted: 02/29/2020] [Indexed: 01/26/2023] Open
Abstract
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.
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Key Words
- (quantitative) Structure–activity relationships
- AI, Artificial Intelligence
- AOPs, Adverse Outcome Pathways
- API, Application Programming interface
- CG, coarse-grained (model)
- CNTs, carbon nanotubes
- Computational toxicology
- Engineered nanomaterials
- FAIR, Findable Accessible Inter-operable and Re-usable
- GUI, Graphical Processing Unit
- HOMO-LUMO, Highest Occupied Molecular Orbital Lowest Unoccupied Molecular Orbital
- Hazard assessment
- IATA, Integrated Approaches to Testing and Assessment
- Integrated approach for testing and assessment
- KE, key events
- MIE, molecular initiating events
- ML, machine learning
- MOA, mechanism (mode) of action
- MWCNT, multi-walled carbon nanotubes
- Machine learning
- NMs, nanomaterials
- Nanoinformatics
- OECD, Organisation for Economic Co-operation and Development
- PBPK, Physiologically Based PharmacoKinetics
- PC, Protein Corona
- PChem, Physicochemical
- PTGS, Predictive Toxicogenomics Space
- Predictive modelling
- QC, quantum-chemical
- QM, quantum-mechanical
- QSAR, quantitative structure-activity relationship
- QSPR, quantitative structure-property relationship
- RA, risk assessment
- REST, Representational State Transfer
- ROS, reactive oxygen species
- Read across
- SAR, structure-activity relationship
- SMILES, Simplified Molecular Input Line Entry System
- SOPs, standard operating procedures
- Safe-by-design
- Toxicogenomics
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Affiliation(s)
| | | | | | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Laura-Jayne A. Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Dario Greco
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | | | | | - Roland Grafström
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Pekka Kohonen
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Penny Nymark
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics – BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | | | - Alexander Lyubartsev
- Institutionen för material- och miljökemi, Stockholms Universitet, 106 91 Stockholm, Sweden
| | - Keld Alstrup Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark
| | - Jan Gerit Brandenburg
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany
- Chief Digital Organization, Merck KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Stephen Lofts
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Claus Svendsen
- UK Centre for Ecology and Hydrology, MacLean Bldg, Benson Ln, Crowmarsh Gifford, Wallingford OX10 8BB, UK
| | - Samuel Harrison
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany
| | - Kaido Tamm
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Lauri Sikk
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Maria Dusinska
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Eleonora Longhin
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Elise Rundén-Pran
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Espen Mariussen
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Naouale El Yamani
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Wolfgang Unger
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Jörg Radnik
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Alexander Tropsha
- Eschelman School of Pharmacy, University of North Carolina at Chapel Hill, 100K Beard Hall, CB# 7568, Chapel Hill, NC 27955-7568, USA
| | - Yoram Cohen
- Samueli School Of Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, CA 90095, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J. R. Lynch Street, Jackson, MS 39217, USA
| | - Christine Ogilvie Hendren
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - Mark Wiesner
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - David Winkler
- La Trobe Institute of Molecular Sciences, La Trobe University, Plenty Rd & Kingsbury Dr, Bundoora, VIC 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- CSIRO Data61, Clayton 3168, Australia
- School of Pharmacy, University of Nottingham, Nottingham, UK
| | - Noriyuki Suzuki
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Jang-Sik Choi
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
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10
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
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11
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Nymark P, Bakker M, Dekkers S, Franken R, Fransman W, García-Bilbao A, Greco D, Gulumian M, Hadrup N, Halappanavar S, Hongisto V, Hougaard KS, Jensen KA, Kohonen P, Koivisto AJ, Dal Maso M, Oosterwijk T, Poikkimäki M, Rodriguez-Llopis I, Stierum R, Sørli JB, Grafström R. Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1904749. [PMID: 31913582 DOI: 10.1002/smll.201904749] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/09/2019] [Indexed: 06/10/2023]
Abstract
Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.
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Affiliation(s)
- Penny Nymark
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Martine Bakker
- National Institute for Public Health and the Environment, RIVM, P.O. Box 1, 3720 BA, Bilthoven, The Netherlands
| | - Susan Dekkers
- National Institute for Public Health and the Environment, RIVM, P.O. Box 1, 3720 BA, Bilthoven, The Netherlands
| | - Remy Franken
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Wouter Fransman
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Amaia García-Bilbao
- GAIKER Technology Centre, Parque Tecnológico, Ed. 202, 48170, Zamudio, Bizkaia, Spain
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Helsinki, Finland
| | - Mary Gulumian
- National Institute for Occupational Health, 25 Hospital St, Constitution Hill, 2000, Johannesburg, South Africa
- Haematology and Molecular Medicine Department, University of the Witwatersrand, 7 York Road, Parktown, 2193, Johannesburg, South Africa
| | - Niels Hadrup
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, 50 Colombine Driveway, Ottawa, ON, K1A 0K9, Canada
| | - Vesa Hongisto
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Karin Sørig Hougaard
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Keld Alstrup Jensen
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Pekka Kohonen
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Antti Joonas Koivisto
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Miikka Dal Maso
- Aerosol Physics Laboratory, Physics Unit, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
| | - Thies Oosterwijk
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Mikko Poikkimäki
- Aerosol Physics Laboratory, Physics Unit, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
| | | | - Rob Stierum
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Jorid Birkelund Sørli
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Roland Grafström
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
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12
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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13
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
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14
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Dong J, Zhu MF, Yun YH, Lu AP, Hou TJ, Cao DS. BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study. Brief Bioinform 2019; 22:474-484. [PMID: 31885044 DOI: 10.1093/bib/bbz150] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 10/22/2019] [Accepted: 10/30/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.
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Affiliation(s)
- Jie Dong
- National Engineering Laboratory for Deep Processing of Rice and Byproducts, Hunan Key Laboratory of Processed Food for Special Medical Purpose, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha,410003 P. R. China.,Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China
| | - Min-Feng Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China
| | - Yong-Huan Yun
- College of Food Science and Engineering, Hainan University, Haikou, 570228 PR China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China.,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
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15
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Cardoso-Silva J, Papageorgiou LG, Tsoka S. Network-based piecewise linear regression for QSAR modelling. J Comput Aided Mol Des 2019; 33:831-844. [PMID: 31628660 PMCID: PMC6825651 DOI: 10.1007/s10822-019-00228-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/28/2019] [Indexed: 02/07/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.
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Affiliation(s)
- Jonathan Cardoso-Silva
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London, WC1E 7JE, UK
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK.
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16
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Fernandez-Lozano C, Carballal A, Machado P, Santos A, Romero J. Visual complexity modelling based on image features fusion of multiple kernels. PeerJ 2019; 7:e7075. [PMID: 31346494 PMCID: PMC6642794 DOI: 10.7717/peerj.7075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 05/04/2019] [Indexed: 01/28/2023] Open
Abstract
Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.
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Affiliation(s)
- Carlos Fernandez-Lozano
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Adrian Carballal
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Penousal Machado
- CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Antonino Santos
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Juan Romero
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
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17
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Forest V, Hochepied JF, Pourchez J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chem Res Toxicol 2019; 32:1320-1326. [PMID: 31243983 DOI: 10.1021/acs.chemrestox.9b00022] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| | - Jean-François Hochepied
- MINES ParisTech , PSL Research University , MAT - Centre des matériaux, CNRS UMR 7633 , BP 87 91003 Evry , France.,UCP, ENSTA ParisTech , Université Paris-Saclay , 828 bd des Maréchaux , 91762 Palaiseau cedex , France
| | - Jérémie Pourchez
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
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18
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Chen T, Brewster P, Tuttle KR, Dworkin LD, Henrich W, Greco BA, Steffes M, Tobe S, Jamerson K, Pencina K, Massaro JM, D'Agostino RB, Cutlip DE, Murphy TP, Cooper CJ, Shapiro JI. Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study. Int J Nephrol Renovasc Dis 2019; 12:49-58. [PMID: 30962703 PMCID: PMC6433104 DOI: 10.2147/ijnrd.s194727] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. Methods We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R. Results The best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models. Conclusion Prediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome. Clinical Trial Registration ClinicalTrials.gov, NCT00081731
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Affiliation(s)
- Tian Chen
- University of Toledo, Toledo, OH, USA
| | | | | | | | - William Henrich
- University of Texas Health Science Center, San Antonio, TX, USA
| | | | | | | | | | - Karol Pencina
- Harvard Clinical Research Institute, Boston University, Boston, MA, USA
| | - Joseph M Massaro
- Harvard Clinical Research Institute, Boston University, Boston, MA, USA
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19
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Differential Gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research. LEARNING AND ANALYTICS IN INTELLIGENT SYSTEMS 2019. [DOI: 10.1007/978-3-030-15628-2_3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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20
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Ash JR, Hughes-Oliver JM. chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models. J Cheminform 2018; 10:57. [PMID: 30488298 PMCID: PMC6755574 DOI: 10.1186/s13321-018-0309-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 11/13/2018] [Indexed: 12/23/2022] Open
Abstract
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation, where blocking increases precision and multiplicity adjustments are applied. chemmodlab is freely available on CRAN at https://cran.r-project.org/web/packages/chemmodlab/index.html.
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Affiliation(s)
- Jeremy R Ash
- Department of Statistics, Bioinformatics Research Center, North Carolina State University, 335 Ricks Hall, Campus Box 7566, Raleigh, NC, 27695-7566, USA.
| | - Jacqueline M Hughes-Oliver
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Campus Box 8203, Raleigh, NC, 27695-8203, USA
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21
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Cardoso‐Silva J, Papadatos G, Papageorgiou LG, Tsoka S. Optimal Piecewise Linear Regression Algorithm for QSAR Modelling. Mol Inform 2018; 38:e1800028. [DOI: 10.1002/minf.201800028] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/02/2018] [Indexed: 12/20/2022]
Affiliation(s)
- Jonathan Cardoso‐Silva
- Department of Informatics, Faculty of Natural and Mathematical SciencesKing's College London, Bush House London WC2B 4BG UK
| | - George Papadatos
- European Molecular Biology Laboratory – European Bioinformatics InstituteWellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD UK
- GlaxoSmithKline Gunnels Wood Road Stevenage, Hertfordshire SG1 2NY UK
| | - Lazaros G. Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical EngineeringUniversity College London Torrington Place London WC1E 7JE UK
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical SciencesKing's College London, Bush House London WC2B 4BG UK
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22
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Varsou DD, Tsiliki G, Nymark P, Kohonen P, Grafström R, Sarimveis H. toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data. J Chem Inf Model 2017; 58:543-549. [DOI: 10.1021/acs.jcim.7b00160] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Georgia Tsiliki
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
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23
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Sfakianakis P, Tzia C. Flavour profiling by gas chromatography–mass spectrometry and sensory analysis of yoghurt derived from ultrasonicated and homogenised milk. Int Dairy J 2017. [DOI: 10.1016/j.idairyj.2017.08.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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24
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González-Durruthy M, Monserrat JM, Rasulev B, Casañola-Martín GM, Barreiro Sorrivas JM, Paraíso-Medina S, Maojo V, González-Díaz H, Pazos A, Munteanu CR. Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra. NANOMATERIALS 2017; 7:nano7110386. [PMID: 29137126 PMCID: PMC5707603 DOI: 10.3390/nano7110386] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 11/06/2017] [Accepted: 11/08/2017] [Indexed: 11/16/2022]
Abstract
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Science (ICB), Federal University of Rio Grande, Rio Grande, RS 96270-900, Brazil.
| | - Jose M Monserrat
- Institute of Biological Science (ICB), Federal University of Rio Grande, Rio Grande, RS 96270-900, Brazil.
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University (NDSU), Fargo, ND 58102, USA.
| | | | - José María Barreiro Sorrivas
- Computer Science School (ETSIINF), Polytechnic University of Madrid (UPM), Calle de losCiruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Sergio Paraíso-Medina
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Víctor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Leioa, Biscay, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain.
| | - Alejandro Pazos
- INIBIC Institute of Biomedical Research, CHUAC, UDC, 15006 Coruña, Spain.
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruña, 15071 Coruña, Spain.
| | - Cristian R Munteanu
- INIBIC Institute of Biomedical Research, CHUAC, UDC, 15006 Coruña, Spain.
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruña, 15071 Coruña, Spain.
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25
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González-Durruthy M, Werhli AV, Seus V, Machado KS, Pazos A, Munteanu CR, González-Díaz H, Monserrat JM. Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Sci Rep 2017; 7:13271. [PMID: 29038520 PMCID: PMC5643473 DOI: 10.1038/s41598-017-13691-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/25/2017] [Indexed: 01/30/2023] Open
Abstract
The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil.
| | - Adriano V Werhli
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Vinicius Seus
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Karina S Machado
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain.,RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - José M Monserrat
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
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26
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Dong J, Yao ZJ, Zhu MF, Wang NN, Lu B, Chen AF, Lu AP, Miao H, Zeng WB, Cao DS. ChemSAR: an online pipelining platform for molecular SAR modeling. J Cheminform 2017; 9:27. [PMID: 29086046 PMCID: PMC5418185 DOI: 10.1186/s13321-017-0215-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 04/24/2017] [Indexed: 12/31/2022] Open
Abstract
Background In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers. Results This work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files. Conclusion ChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Zhi-Jiang Yao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Min-Feng Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ning-Ning Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Ben Lu
- The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Alex F Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China
| | - Hongyu Miao
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wen-Bin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China. .,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China.
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27
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Fernandez-Lozano C, Gestal M, Munteanu CR, Dorado J, Pazos A. A methodology for the design of experiments in computational intelligence with multiple regression models. PeerJ 2016; 4:e2721. [PMID: 27920952 PMCID: PMC5136129 DOI: 10.7717/peerj.2721] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 10/25/2016] [Indexed: 01/23/2023] Open
Abstract
The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.
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Affiliation(s)
- Carlos Fernandez-Lozano
- Information and Communications Technologies Department, University of A Coruna , A Coruña , Spain
| | - Marcos Gestal
- Information and Communications Technologies Department, University of A Coruna , A Coruña , Spain
| | - Cristian R Munteanu
- Information and Communications Technologies Department, University of A Coruna , A Coruña , Spain
| | - Julian Dorado
- Information and Communications Technologies Department, University of A Coruna , A Coruña , Spain
| | - Alejandro Pazos
- Information and Communications Technologies Department, University of A Coruna, A Coruña, Spain; Complexo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigacion Biomedica de A Coruña (INIBIC), A Coruña, Spain
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Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory. Sci Rep 2016; 6:30174. [PMID: 27460882 PMCID: PMC4962052 DOI: 10.1038/srep30174] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 06/28/2016] [Indexed: 12/16/2022] Open
Abstract
The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.
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Qiu T, Qiu J, Feng J, Wu D, Yang Y, Tang K, Cao Z, Zhu R. The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope. Brief Bioinform 2016; 18:125-136. [PMID: 26873661 DOI: 10.1093/bib/bbw004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 12/09/2015] [Indexed: 12/17/2022] Open
Abstract
As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.
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