Supplementary Table S1. Control Capsules (CC) and Study Capsules (CR) Composition
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Supporting Information
Supporting Information Celhar et al. 10.1073/pnas.1507052112 SI Materials and Methods using a Nanodrop spectrophotometer (Thermo Fisher Scien- Proteinuria. Proteinuria was assessed using Albustix (Bayer). Al- tific). A TaqMan RNA-to-CT 1-Step Kit (Applied Biosystems) bumin levels in urine were assayed using an Albumin Mouse was used to perform the reverse transcription and quantitative ELISA Kit (Abcam) according to the manufacturer’s instructions; PCR reactions according to the manufacturer’s instructions samples were assayed at a dilution of 1:400. Samples were nor- using TaqMan gene expression assays (Applied Biosystems) to malized for creatinine using a Creatinine (urinary) Colorimetric either Tlr7 (Mm00446590) or the B2m housekeeping gene Assay Kit (Cayman Chemical) according to the manufacturer’s (Mm00437762). Real-time PCR was performed on the 7900H instructions; initial sample dilution of 1:10. fast real-time PCR system and analyzed using SDS 2.4 (Applied Biosystems). Relative mRNA expression was calculated using the Cell Sorting, RNA Isolation, and RT-PCR. Splenic B cells were comparative C method. + − + + t sorted as live CD45 Gr1 B220 CD19 , splenic T cells as live + − + + CD45 Gr1 CD3 CD5 and peritoneal macrophages as live Imaging. Kidney sections from OCT embedded tissue were fixed + − CD45 Gr1 CD11bhiF4/80hi. Sorted cells were centrifuged, re- with 4% paraformaldehyde before permeabilization with acetone suspended in TRIzol (Life Technologies) and stored at −80°. RNA and stained with Phalloidin (AF647) and anti-CD3d (unlabeled was extracted by TRIzol/chloroform and purified with the Qiagen Ab followed by secondary staining with donkey anti-goat Dylight RNeasy Mini purification kit according to the manufacturer’s 550). -
PTK2B (Human) Recombinant Protein
PTK2B (Human) Recombinant PKB, PTK, PYK2, RAFTK Protein Gene Summary: This gene encodes a cytoplasmic protein tyrosine kinase which is involved in Catalog Number: P5800 calcium-induced regulation of ion channels and Regulation Status: For research use only (RUO) activation of the map kinase signaling pathway. The encoded protein may represent an important signaling Product Description: Human PTK2B (NP_775267.1, 1 intermediate between neuropeptide-activated receptors a.a. - 967 a.a.) full-length recombinant protein with GST or neurotransmitters that increase calcium flux and the tag expressed in Baculovirus infected Sf21 cells. downstream signals that regulate neuronal activity. The encoded protein undergoes rapid tyrosine Host: Insect phosphorylation and activation in response to increases in the intracellular calcium concentration, nicotinic Theoretical MW (kDa): 138 acetylcholine receptor activation, membrane depolarization, or protein kinase C activation. This Applications: Func, SDS-PAGE protein has been shown to bind CRK-associated (See our web site product page for detailed applications substrate, nephrocystin, GTPase regulator associated information) with FAK, and the SH2 domain of GRB2. The encoded protein is a member of the FAK subfamily of protein Protocols: See our web site at tyrosine kinases but lacks significant sequence similarity http://www.abnova.com/support/protocols.asp or product to kinases from other subfamilies. Four transcript page for detailed protocols variants encoding two different isoforms have been Form: Liquid found for this gene. [provided by RefSeq] Preparation Method: Baculovirus infected insect cell (Sf21) expression system Purification: Glutathione sepharose chromatography Purity: 74 % by SDS-PAGE/CBB staining Activity: The activity was measured by off-chip mobility shift assay. -
Mechanism of Action Through an IFN Type I-Independent Responses To
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 is online at: average * The Journal of Immunology , 12 of which you can access for free at: 2012; 188:3088-3098; Prepublished online 20 from submission to initial decision 4 weeks from acceptance to publication February 2012; doi: 10.4049/jimmunol.1101764 http://www.jimmunol.org/content/188/7/3088 MF59 and Pam3CSK4 Boost Adaptive Responses to Influenza Subunit Vaccine through an IFN Type I-Independent Mechanism of Action Elena Caproni, Elaine Tritto, Mario Cortese, Alessandro Muzzi, Flaviana Mosca, Elisabetta Monaci, Barbara Baudner, Anja Seubert and Ennio De Gregorio J Immunol cites 33 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2012/02/21/jimmunol.110176 4.DC1 This article http://www.jimmunol.org/content/188/7/3088.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 -
Supplementary Materials
Supplementary Suppl. Figure 1: MAPK signalling pathway of A: NCI-H2502, B: NCI-H2452, C: MSTO-211H and D: MRC-5. Suppl. Figure 2: Cell cycle pathway of A: NCI-H2502, B: NCI-H2452, C: MSTO-211H and D: MRC- 5. Suppl. Figure 3: Cancer pathways of A: NCI-H2502, B: NCI-H2452, C: MSTO-211H and D: MRC-5. Suppl. Figure 4: Phosphorylation level of A: ARAF, B: EPHA1, C: EPHA2, D: EPHA7 in all cell lines. For each cell line, phosphorylation levels are depicted before (Medium) and after cisplatin treatment (Cis). Suppl. Figure 5: Phosphorylation Level of A: KIT, B: PTPN11, C: PIK3R1, D: PTPN6 in all cell lines. For each cell line, phosphorylation levels are depicted before (Medium) and after cisplatin treatment (Cis). Suppl. Figure 6: Phosphorylation Level of A: KDR, B: EFS, C: AKT1, D: PTK2B/FAK2 in all cell lines. For each cell line, phosphorylation levels are depicted before (Medium) and after cisplatin treatment (Cis). Suppl. Figure 7: Scoreplots and volcanoplots of PTK upstream kinase analysis: A: Scoreplot of PTK- Upstream kinase analysis for NCI-H2052 cells. B: Volcanoplot of PTK-Upstream kinase analysis for NCI-H2052 cells. C: Scoreplot of PTK-Upstream kinase analysis for NCI-H2452 cells. D: Volcanoplot of PTK-Upstream kinase analysis for NCI-H2452 cells. E: Scoreplot of PTK-Upstream kinase analysis for MSTO-211H cells. F: Volcanoplot of PTK-Upstream kinase analysis for MSTO- 211H cells. G: Scoreplot of PTK-Upstream kinase analysis for MRC-5cells. H: Volcanoplot of PTK- Upstream kinase analysis for MRC-5 cells. Suppl. Figure 8: Scoreplots and volcanoplots of STK upstream kinase analysis: A: Scoreplot of STK- Upstream kinase analysis for NCI-H2052 cells. -
Supplementary Material
Supplementary Material Table S1: Significant downregulated KEGGs pathways identified by DAVID following exposure to five cinnamon- based phenylpropanoids (p < 0.05). p-value Term: Genes (Benjamini) Cytokine-cytokine receptor interaction: FASLG, TNFSF14, CXCL11, IL11, FLT3LG, CCL3L1, CCL3L3, CXCR6, XCR1, 2.43 × 105 RTEL1, CSF2RA, TNFRSF17, TNFRSF14, CCNL2, VEGFB, AMH, TNFRSF10B, INHBE, IFNB1, CCR3, VEGFA, CCR2, IL12A, CCL1, CCL3, CXCL5, TNFRSF25, CCR1, CSF1, CX3CL1, CCL7, CCL24, TNFRSF1B, IL12RB1, CCL21, FIGF, EPO, IL4, IL18R1, FLT1, TGFBR1, EDA2R, HGF, TNFSF8, KDR, LEP, GH2, CCL13, EPOR, XCL1, IFNA16, XCL2 Neuroactive ligand-receptor interaction: OPRM1, THRA, GRIK1, DRD2, GRIK2, TACR2, TACR1, GABRB1, LPAR4, 9.68 × 105 GRIK5, FPR1, PRSS1, GNRHR, FPR2, EDNRA, AGTR2, LTB4R, PRSS2, CNR1, S1PR4, CALCRL, TAAR5, GABRE, PTGER1, GABRG3, C5AR1, PTGER3, PTGER4, GABRA6, GABRA5, GRM1, PLG, LEP, CRHR1, GH2, GRM3, SSTR2, Chlorogenic acid Chlorogenic CHRM3, GRIA1, MC2R, P2RX2, TBXA2R, GHSR, HTR2C, TSHR, LHB, GLP1R, OPRD1 Hematopoietic cell lineage: IL4, CR1, CD8B, CSF1, FCER2, GYPA, ITGA2, IL11, GP9, FLT3LG, CD38, CD19, DNTT, 9.29 × 104 GP1BB, CD22, EPOR, CSF2RA, CD14, THPO, EPO, HLA-DRA, ITGA2B Cytokine-cytokine receptor interaction: IL6ST, IL21R, IL19, TNFSF15, CXCR3, IL15, CXCL11, TGFB1, IL11, FLT3LG, CXCL10, CCR10, XCR1, RTEL1, CSF2RA, IL21, CCNL2, VEGFB, CCR8, AMH, TNFRSF10C, IFNB1, PDGFRA, EDA, CXCL5, TNFRSF25, CSF1, IFNW1, CNTFR, CX3CL1, CCL5, TNFRSF4, CCL4, CCL27, CCL24, CCL25, CCL23, IFNA6, IFNA5, FIGF, EPO, AMHR2, IL2RA, FLT4, TGFBR2, EDA2R, -
Blood Vitronectin Is a Major Activator of LIF and IL-6 in the Brain Through Integrin–FAK and Upar Signaling Matthew P
© 2018. Published by The Company of Biologists Ltd | Journal of Cell Science (2018) 131, jcs202580. doi:10.1242/jcs.202580 RESEARCH ARTICLE Blood vitronectin is a major activator of LIF and IL-6 in the brain through integrin–FAK and uPAR signaling Matthew P. Keasey1, Cuihong Jia1, Lylyan F. Pimentel1,2, Richard R. Sante1, Chiharu Lovins1 and Theo Hagg1,* ABSTRACT Microglia and astrocytes express the VTN receptors αvβ3 and α β We defined how blood-derived vitronectin (VTN) rapidly and potently v 5 integrin (Herrera-Molina et al., 2012; Kang et al., 2008; activates leukemia inhibitory factor (LIF) and pro-inflammatory Milner, 2009; Welser-Alves et al., 2011). Microglia and astrocytes, interleukin 6 (IL-6) in vitro and after vascular injury in the brain. as well as endothelial cells, are major producers of pro- α in vitro Treatment with VTN (but not fibrinogen, fibronectin, laminin-111 or inflammatory cytokines, such as IL-6 and TNF , and collagen-I) substantially increased LIF and IL-6 within 4 h in after traumatic or ischemic injury to the brain (Banner et al., 1997; C6-astroglioma cells, while VTN−/− mouse plasma was less effective Erta et al., 2012; Lau and Yu, 2001) or upon self-induction by IL-6 than that from wild-type mice. LIF and IL-6 were induced by (Van Wagoner and Benveniste, 1999). IL-6 is a major regulator of a intracerebral injection of recombinant human (rh)VTN in mice, but variety of inflammatory disorders and a target for therapies (Hunter induction seen upon intracerebral hemorrhage was less in VTN−/− and Jones, 2015). -
PRKACA Mediates Resistance to HER2-Targeted Therapy in Breast Cancer Cells and Restores Anti-Apoptotic Signaling
Oncogene (2015) 34, 2061–2071 © 2015 Macmillan Publishers Limited All rights reserved 0950-9232/15 www.nature.com/onc ORIGINAL ARTICLE PRKACA mediates resistance to HER2-targeted therapy in breast cancer cells and restores anti-apoptotic signaling SE Moody1,2,3, AC Schinzel1, S Singh1, F Izzo1, MR Strickland1, L Luo1,2, SR Thomas3, JS Boehm3, SY Kim4, ZC Wang5,6 and WC Hahn1,2,3 Targeting HER2 with antibodies or small molecule inhibitors in HER2-positive breast cancer leads to improved survival, but resistance is a common clinical problem. To uncover novel mechanisms of resistance to anti-HER2 therapy in breast cancer, we performed a kinase open reading frame screen to identify genes that rescue HER2-amplified breast cancer cells from HER2 inhibition or suppression. In addition to multiple members of the MAPK (mitogen-activated protein kinase) and PI3K (phosphoinositide 3-kinase) signaling pathways, we discovered that expression of the survival kinases PRKACA and PIM1 rescued cells from anti-HER2 therapy. Furthermore, we observed elevated PRKACA expression in trastuzumab-resistant breast cancer samples, indicating that this pathway is activated in breast cancers that are clinically resistant to trastuzumab-containing therapy. We found that neither PRKACA nor PIM1 restored MAPK or PI3K activation after lapatinib or trastuzumab treatment, but rather inactivated the pro-apoptotic protein BAD, the BCl-2-associated death promoter, thereby permitting survival signaling through BCL- XL. Pharmacological blockade of BCL-XL/BCL-2 partially abrogated the rescue effects conferred by PRKACA and PIM1, and sensitized cells to lapatinib treatment. These observations suggest that combined targeting of HER2 and the BCL-XL/BCL-2 anti-apoptotic pathway may increase responses to anti-HER2 therapy in breast cancer and decrease the emergence of resistant disease. -
Diverse Functional Autoantibodies in Patients with COVID-19
medRxiv preprint doi: https://doi.org/10.1101/2020.12.10.20247205; this version posted December 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Diverse Functional Autoantibodies in Patients with COVID-19 Eric Y. Wang1,*, Tianyang Mao1,*, Jon Klein1,*, Yile Dai1,*, John D. Huck1, Feimei Liu1, Neil S. Zheng1, Ting Zhou1, Benjamin Israelow1, Patrick Wong1, Carolina Lucas1, Julio Silva1, Ji Eun Oh1, Eric Song1, Emily S. Perotti1, Suzanne Fischer1, Melissa Campbell5, John B. Fournier5, Anne L. Wyllie3, Chantal B. F. Vogels3, Isabel M. Ott3, Chaney C. Kalinich3, Mary E. Petrone3, Anne E. Watkins3, Yale IMPACT Team¶, Charles Dela Cruz4, Shelli F. Farhadian5, Wade L. Schulz6,7, Nathan D. Grubaugh3, Albert I. Ko3,5, Akiko Iwasaki1,3,8,#, Aaron M. Ring1,2,# 1 Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA 2 Department of Pharmacology, Yale School of Medicine, New Haven, CT, USA 3 Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA 4 Department of Medicine, Section of Pulmonary and Critical Care Medicine, Yale School of Medicine, New Haven, CT, USA 5 Department of Internal Medicine (Infectious Diseases), Yale School of Medicine, New Haven, CT, USA 6 Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA 7 Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA 8 Howard Hughes Medical Institute, Chevy Chase, MD, USA * These authors contributed equally to this work ¶ A list of authors and their affiliations appears at the end of the paper # Correspondence: [email protected] (A.M.R.); [email protected] (A.I.) 1 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. -
Supplementary Table 1. in Vitro Side Effect Profiling Study for LDN/OSU-0212320. Neurotransmitter Related Steroids
Supplementary Table 1. In vitro side effect profiling study for LDN/OSU-0212320. Percent Inhibition Receptor 10 µM Neurotransmitter Related Adenosine, Non-selective 7.29% Adrenergic, Alpha 1, Non-selective 24.98% Adrenergic, Alpha 2, Non-selective 27.18% Adrenergic, Beta, Non-selective -20.94% Dopamine Transporter 8.69% Dopamine, D1 (h) 8.48% Dopamine, D2s (h) 4.06% GABA A, Agonist Site -16.15% GABA A, BDZ, alpha 1 site 12.73% GABA-B 13.60% Glutamate, AMPA Site (Ionotropic) 12.06% Glutamate, Kainate Site (Ionotropic) -1.03% Glutamate, NMDA Agonist Site (Ionotropic) 0.12% Glutamate, NMDA, Glycine (Stry-insens Site) 9.84% (Ionotropic) Glycine, Strychnine-sensitive 0.99% Histamine, H1 -5.54% Histamine, H2 16.54% Histamine, H3 4.80% Melatonin, Non-selective -5.54% Muscarinic, M1 (hr) -1.88% Muscarinic, M2 (h) 0.82% Muscarinic, Non-selective, Central 29.04% Muscarinic, Non-selective, Peripheral 0.29% Nicotinic, Neuronal (-BnTx insensitive) 7.85% Norepinephrine Transporter 2.87% Opioid, Non-selective -0.09% Opioid, Orphanin, ORL1 (h) 11.55% Serotonin Transporter -3.02% Serotonin, Non-selective 26.33% Sigma, Non-Selective 10.19% Steroids Estrogen 11.16% 1 Percent Inhibition Receptor 10 µM Testosterone (cytosolic) (h) 12.50% Ion Channels Calcium Channel, Type L (Dihydropyridine Site) 43.18% Calcium Channel, Type N 4.15% Potassium Channel, ATP-Sensitive -4.05% Potassium Channel, Ca2+ Act., VI 17.80% Potassium Channel, I(Kr) (hERG) (h) -6.44% Sodium, Site 2 -0.39% Second Messengers Nitric Oxide, NOS (Neuronal-Binding) -17.09% Prostaglandins Leukotriene, -
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int. -
Gene Expression Profiling of Chronic Myeloid Leukemia with Variant T(9;22)
Albano et al. Molecular Cancer 2013, 12:36 http://www.molecular-cancer.com/content/12/1/36 SHORT COMMUNICATION Open Access Gene expression profiling of chronic myeloid leukemia with variant t(9;22) reveals a different signature from cases with classic translocation Francesco Albano*, Antonella Zagaria, Luisa Anelli, Nicoletta Coccaro, Luciana Impera, Crescenzio Francesco Minervini, Angela Minervini, Antonella Russo Rossi, Giuseppina Tota, Paola Casieri and Giorgina Specchia Abstract Background: The t(9;22)(q34;q11) generating the BCR/ABL1 fusion gene represents the cytogenetic hallmark of chronic myeloid leukemia (CML). About 5–10% of CML cases show variant translocations with the involvement of other chromosomes in addition to chromosomes 9 and 22. The molecular bases of biological differences between CML patients with classic and variant t(9;22) have never been clarified. Findings: In this study, we performed gene expression microarray analysis to compare CML patients bearing variant rearrangements and those with classic t(9;22)(q34;q11). We identified 59 differentially expressed genes significantly associated with the two analyzed groups. The role of specific candidate genes such as TRIB1 (tribbles homolog 1), PTK2B (protein tyrosine kinase 2 beta), and C5AR1 (complement component 5a receptor 1) is discussed. Conclusions: Our results reveal that in CML cases with variant t(9;22) there is an enhancement of the MAPK pathway deregulation and show that kinases are a common target of molecular alterations in hematological disorders. Keywords: Chronic myeloid leukemia, Variant t(9;22) rearrangements, Gene expression profiling, Protein kinases, Cellular pathways Background BCR has been reported in CML cases with variant trans- Chronic myeloid leukemia (CML) is a myeloprolife- locations with a greater frequency (30-40%) than in rative disorder derived from hematopoietic stem cell cases with classic t(9;22) (10-18%) [6,7]. -
Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach
Cancers 2019, 11, x S1 of S9 Supplementary Materials: Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach Young Rae Kim, Yong Wan Kim, Suh Eun Lee, Hye Won Yang and Sung Young Kim Table S1. Characteristics of individual studies. Sample Size Origin of Cancer Drug Dataset Platform S AR (Cell Lines) Lung cancer Agilent-014850 Whole Human GSE34228 26 26 (PC9) Genome Microarray 4x44K Gefitinib Epidermoid carcinoma Affymetrix Human Genome U133 GSE10696 3 3 (A431) Plus 2.0 Head and neck cancer Illumina HumanHT-12 V4.0 GSE62061 12 12 (Cal-27, SSC-25, FaDu, expression beadchip SQ20B) Erlotinib Head and neck cancer Illumina HumanHT-12 V4.0 GSE49135 3 3 (HN5) expression beadchip Lung cancer (HCC827, Illumina HumanHT-12 V3.0 GSE38310 3 6 ER3, T15-2) expression beadchip Lung cancer Illumina HumanHT-12 V3.0 GSE62504 1 2 (HCC827) expression beadchip Afatinib Lung cancer * Illumina HumanHT-12 V4.0 GSE75468 1 3 (HCC827) expression beadchip Head and neck cancer Affymetrix Human Genome U133 Cetuximab GSE21483 3 3 (SCC1) Plus 2.0 Array GEO, gene expression omnibus; GSE, gene expression series; S, sensitive; AR, acquired EGFR-TKI resistant; * Lung Cancer Cells Derived from Tumor Xenograft Model. Table S2. The performances of four penalized regression models. Model ACC precision recall F1 MCC AUROC BRIER Ridge 0.889 0.852 0.958 0.902 0.782 0.964 0.129 Lasso 0.944 0.957 0.938 0.947 0.889 0.991 0.042 Elastic Net 0.978 0.979 0.979 0.979 0.955 0.999 0.023 EPSGO Elastic Net 0.989 1.000 0.979 0.989 0.978 1.000 0.018 AUROC, area under curve of receiver operating characteristic; ACC, accuracy; MCC, Matthews correlation coefficient; EPSGO, Efficient Parameter Selection via Global Optimization algorithm.