cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14558944.v1
title: Additional file 2 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 2: Supplementary file 2. Fig. 1 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the contrast recovery data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β100 (A); TOF-OSEM 4i8s5mm and Q.Clear β200 (B); TOF-OSEM 4i8s5mm and Q.Clear β300 (C); TOF-OSEM 4i8s5mm and Q.Clear β400 (D); TOF-OSEM 4i8s5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the contrast recovery data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β600 (F); TOF-OSEM 4i8s5mm and Q.Clear β700 (G); TOF-OSEM 4i8s5mm and Q.Clear β800 (H); TOF-OSEM 4i8s5mm and Q.Clear β900 (I); TOF-OSEM 4i8s5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558944
title: Additional file 2 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 2: Supplementary file 2. Fig. 1 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the contrast recovery data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β100 (A); TOF-OSEM 4i8s5mm and Q.Clear β200 (B); TOF-OSEM 4i8s5mm and Q.Clear β300 (C); TOF-OSEM 4i8s5mm and Q.Clear β400 (D); TOF-OSEM 4i8s5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the contrast recovery data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β600 (F); TOF-OSEM 4i8s5mm and Q.Clear β700 (G); TOF-OSEM 4i8s5mm and Q.Clear β800 (H); TOF-OSEM 4i8s5mm and Q.Clear β900 (I); TOF-OSEM 4i8s5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558947.v1
title: Additional file 3 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 3: Supplementary file 3. Fig. 1 Bland-Altman plots assessing agreement between FBP with 5mm and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. FBP 5mm and Q.Clear β100 (A); FBP 5mm and Q.Clear β200 (B); FBP 5mm and Q.Clear β300 (C); FBP 5mm and Q.Clear β400 (D); FBP 5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between FBP with 5mm and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. FBP 5mm and Q.Clear β600 (F); FBP 5mm and Q.Clear β700 (G); FBP 5mm and Q.Clear β800 (H); FBP 5mm and Q.Clear β900 (I); FBP 5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558950.v1
title: Additional file 4 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 4: Supplementary file 4. Fig. 1 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β100 (A); TOF-OSEM 4i8s5mm and Q.Clear β200 (B); TOF-OSEM 4i8s5mm and Q.Clear β300 (C); TOF-OSEM 4i8s5mm and Q.Clear β400 (D); TOF-OSEM 4i8s5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm)and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β600 (F); TOF-OSEM 4i8s5mm and Q.Clear β700 (G); TOF-OSEM 4i8s5mm and Q.Clear β800 (H); TOF-OSEM 4i8s5mm and Q.Clear β900 (I); TOF-OSEM 4i8s5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558947
title: Additional file 3 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 3: Supplementary file 3. Fig. 1 Bland-Altman plots assessing agreement between FBP with 5mm and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. FBP 5mm and Q.Clear β100 (A); FBP 5mm and Q.Clear β200 (B); FBP 5mm and Q.Clear β300 (C); FBP 5mm and Q.Clear β400 (D); FBP 5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between FBP with 5mm and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. FBP 5mm and Q.Clear β600 (F); FBP 5mm and Q.Clear β700 (G); FBP 5mm and Q.Clear β800 (H); FBP 5mm and Q.Clear β900 (I); FBP 5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558950
title: Additional file 4 of Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

description: Additional file 4: Supplementary file 4. Fig. 1 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm) and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β100 (A); TOF-OSEM 4i8s5mm and Q.Clear β200 (B); TOF-OSEM 4i8s5mm and Q.Clear β300 (C); TOF-OSEM 4i8s5mm and Q.Clear β400 (D); TOF-OSEM 4i8s5mm and Q.Clear β500 (E). Fig. 2 Bland-Altman plots assessing agreement between TOF-OSEM 4iteration, 8subsets with 5mm (4i8s5mm)and Q.Clear with different β values, for the background variability data obtained from the NEMA phantom. TOF-OSEM 4i8s5mm and Q.Clear β600 (F); TOF-OSEM 4i8s5mm and Q.Clear β700 (G); TOF-OSEM 4i8s5mm and Q.Clear β800 (H); TOF-OSEM 4i8s5mm and Q.Clear β900 (I); TOF-OSEM 4i8s5mm and Q.Clear β1000 (J).
DOI: 10.6084/m9.figshare.14558972
title: Additional file 2 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 1:. Baseline characteristics of the independent cohort.
DOI: 10.6084/m9.figshare.14558972.v1
title: Additional file 2 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 1:. Baseline characteristics of the independent cohort.
DOI: 10.6084/m9.figshare.14558975
title: Additional file 3 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 2:. Gut microbiome bacterial species associated with the serum ferritin. Results of differential bacterial abundance associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558975.v1
title: Additional file 3 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 2:. Gut microbiome bacterial species associated with the serum ferritin. Results of differential bacterial abundance associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558978
title: Additional file 4 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 3:. Metagenome functions based on KEGG annotation associated with the serum ferritin. Results of metagenome KEGG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558978.v1
title: Additional file 4 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 3:. Metagenome functions based on KEGG annotation associated with the serum ferritin. Results of metagenome KEGG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558981
title: Additional file 5 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 4:. Metagenome functions based on EggNOG annotation associated with the serum ferritin. Results of metagenome EggNOG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558981.v1
title: Additional file 5 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome

description: Additional file 4:. Metagenome functions based on EggNOG annotation associated with the serum ferritin. Results of metagenome EggNOG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP.
DOI: 10.6084/m9.figshare.14558998
title: Additional file 1 of Impact of photoreceptor density in a 3D simulation of panretinal laser photocoagulation

description: Additional file 1.
DOI: 10.6084/m9.figshare.14558998.v1
title: Additional file 1 of Impact of photoreceptor density in a 3D simulation of panretinal laser photocoagulation

description: Additional file 1.
DOI: 10.6084/m9.figshare.14559055.v1
title: Additional file 1 of scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

description: Additional file 1: Supplementary tables.
DOI: 10.6084/m9.figshare.14559055
title: Additional file 1 of scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

description: Additional file 1: Supplementary tables.
DOI: 10.6084/m9.figshare.14559088
title: Additional file 1 of Association between socioeconomic status and prolonged television viewing time in a general Japanese population: NIPPON DATA2010

description: Additional file 1: Supplement Table 1. Participants’ instrumental activity of daily living and current medical histories by sex and age groups. Supplement Table 2. Mean physical activities time by intensity in sex and age classes. Supplement Table 3. Mean TV viewing time by socioeconomic status.
DOI: 10.6084/m9.figshare.14559088.v1
title: Additional file 1 of Association between socioeconomic status and prolonged television viewing time in a general Japanese population: NIPPON DATA2010

description: Additional file 1: Supplement Table 1. Participants’ instrumental activity of daily living and current medical histories by sex and age groups. Supplement Table 2. Mean physical activities time by intensity in sex and age classes. Supplement Table 3. Mean TV viewing time by socioeconomic status.
DOI: 10.6084/m9.figshare.14559104
title: Additional file 1 of Dual RNAseq highlights the kinetics of skin microbiome and fish host responsiveness to bacterial infection

description: Additional file 1.
DOI: 10.6084/m9.figshare.14559104.v1
title: Additional file 1 of Dual RNAseq highlights the kinetics of skin microbiome and fish host responsiveness to bacterial infection

description: Additional file 1.
DOI: 10.6084/m9.figshare.14559164
title: Additional file 20 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 20: Table S2. Distribution of H3K4me3, H3K9me3 and H3K27me3 on the complete set of P. anserina genes in wild-type background (WT) and in ΔPaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. List of genes where H3K4me3 and H3K27me3 can be found overlapping in the wild-type background. Distribution of H3K4me3, H3K9me3 and H3K27me3 on a subset of P. anserina secondary metabolite gene clusters in wild-type background (WT) and in PaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. ChIP_peaks, 0 means no peaks detected by MACS2, 1 means at least one peak detected by MACS2. ChIP_coverage: mapped reads after normalization (see “ChIP-seq sequencing data analysis and visualization” paragraph in “Methods” for details). All_BRICKS: H3K4me3, H3K9me3 or H3K27me3 domainograms were simplified as described in [50] to a set of discrete domains of enrichment called Blocks of Regulators In Chromosomal Kontext (BRICKs). BRICKS can be used to define domains with significant clustering of ChIP-seq signal and their boundaries, while domainograms provide an overview.
DOI: 10.6084/m9.figshare.14559164.v1
title: Additional file 20 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 20: Table S2. Distribution of H3K4me3, H3K9me3 and H3K27me3 on the complete set of P. anserina genes in wild-type background (WT) and in ΔPaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. List of genes where H3K4me3 and H3K27me3 can be found overlapping in the wild-type background. Distribution of H3K4me3, H3K9me3 and H3K27me3 on a subset of P. anserina secondary metabolite gene clusters in wild-type background (WT) and in PaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. ChIP_peaks, 0 means no peaks detected by MACS2, 1 means at least one peak detected by MACS2. ChIP_coverage: mapped reads after normalization (see “ChIP-seq sequencing data analysis and visualization” paragraph in “Methods” for details). All_BRICKS: H3K4me3, H3K9me3 or H3K27me3 domainograms were simplified as described in [50] to a set of discrete domains of enrichment called Blocks of Regulators In Chromosomal Kontext (BRICKs). BRICKS can be used to define domains with significant clustering of ChIP-seq signal and their boundaries, while domainograms provide an overview.
DOI: 10.6084/m9.figshare.14559167
title: Additional file 21 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 21: Table S3. Distribution of H3K4me3, H3K9me3 and H3K27me3 on the complete set of P. anserina transposable elements in wild-type background (WT) and in ΔPaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. ChIP_peaks, 0 means no peaks detected by MACS2, 1 means at least one peak detected by MACS2. ChIP_coverage: mapped reads after normalization (see “ChIP-seq sequencing data analysis and visualization” paragraph in “Methods” for details).
DOI: 10.6084/m9.figshare.14559170
title: Additional file 22 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 22: Table S4. Cq for RT-qPCR experiments and geNorm analysis of candidate normalization genes.
DOI: 10.6084/m9.figshare.14559170.v1
title: Additional file 22 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 22: Table S4. Cq for RT-qPCR experiments and geNorm analysis of candidate normalization genes.
DOI: 10.6084/m9.figshare.14559167.v1
title: Additional file 21 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 21: Table S3. Distribution of H3K4me3, H3K9me3 and H3K27me3 on the complete set of P. anserina transposable elements in wild-type background (WT) and in ΔPaKmt1, ΔPaKmt6 and ΔPaHP1 mutants. ChIP_peaks, 0 means no peaks detected by MACS2, 1 means at least one peak detected by MACS2. ChIP_coverage: mapped reads after normalization (see “ChIP-seq sequencing data analysis and visualization” paragraph in “Methods” for details).
DOI: 10.6084/m9.figshare.14559176
title: Additional file 24 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 24: Table S6. Accession numbers for proteins used in alignments to build the phylogenic trees.
DOI: 10.6084/m9.figshare.14559176.v1
title: Additional file 24 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 24: Table S6. Accession numbers for proteins used in alignments to build the phylogenic trees.
DOI: 10.6084/m9.figshare.14559179
title: Additional file 25 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 25: Table S7. Heatmap of Spearman correlation coefficient comparison between ChIP-seq samples. Samples are labeled as follow: Genotype_strain_marks. E.g., KMT6-11d_K9 = ΔPaKmt6 strain, from spore number 11d (biological replicate), IP = H3K9me3. Spearman’s correlation coefficient confirms the reproducibility of the experiments since biological replicates cluster together with high correlation coefficients. Moreover, H3K9me3 and H3K27me3 IP share a same cluster that negatively correlates with the H3K4me3 cluster. Both display no correlation with non-IP input and negative sample (Mock and H3K27me3 in the ΔPaKmt6), which means that IP results are significant and specific.
DOI: 10.6084/m9.figshare.14559179.v1
title: Additional file 25 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 25: Table S7. Heatmap of Spearman correlation coefficient comparison between ChIP-seq samples. Samples are labeled as follow: Genotype_strain_marks. E.g., KMT6-11d_K9 = ΔPaKmt6 strain, from spore number 11d (biological replicate), IP = H3K9me3. Spearman’s correlation coefficient confirms the reproducibility of the experiments since biological replicates cluster together with high correlation coefficients. Moreover, H3K9me3 and H3K27me3 IP share a same cluster that negatively correlates with the H3K4me3 cluster. Both display no correlation with non-IP input and negative sample (Mock and H3K27me3 in the ΔPaKmt6), which means that IP results are significant and specific.
DOI: 10.6084/m9.figshare.14559182.v1
title: Additional file 26 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 26: Table S8. Raw data from the heatmap of Spearman’s correlation coefficient comparison in Additional file 18: Fig. S18.
DOI: 10.6084/m9.figshare.14559182
title: Additional file 26 of Loss of EZH2-like or SU(VAR)3–9-like proteins causes simultaneous perturbations in H3K27 and H3K9 tri-methylation and associated developmental defects in the fungus Podospora anserina

description: Additional file 26: Table S8. Raw data from the heatmap of Spearman’s correlation coefficient comparison in Additional file 18: Fig. S18.
DOI: 10.6084/m9.figshare.14559242
title: Additional file 1 of Using next generation sequencing of alpine plants to improve fecal metabarcoding diet analysis for Dall’s sheep

description: Additional file 1. Table of information about the plant specimens used for rbcl sequencing.
DOI: 10.6084/m9.figshare.14559242.v1
title: Additional file 1 of Using next generation sequencing of alpine plants to improve fecal metabarcoding diet analysis for Dall’s sheep

description: Additional file 1. Table of information about the plant specimens used for rbcl sequencing.
DOI: 10.6084/m9.figshare.14559281.v1
title: Additional file 1 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 1. MOCCA source code.
DOI: 10.6084/m9.figshare.14559281
title: Additional file 1 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 1. MOCCA source code.
DOI: 10.6084/m9.figshare.14559290
title: Additional file 4 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 4. Candidate PREs predicted by RF-MOCCA M2019.
DOI: 10.6084/m9.figshare.14559296
title: Additional file 6 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 6. Core PREs predicted by RF-MOCCA M2019.
DOI: 10.6084/m9.figshare.14559293
title: Additional file 5 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 5. Candidate BEs predicted by RF-MOCCA M2020.
DOI: 10.6084/m9.figshare.14559290.v1
title: Additional file 4 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 4. Candidate PREs predicted by RF-MOCCA M2019.
DOI: 10.6084/m9.figshare.14559296.v1
title: Additional file 6 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 6. Core PREs predicted by RF-MOCCA M2019.
DOI: 10.6084/m9.figshare.14559299.v1
title: Additional file 7 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 7. Core BEs predicted by RF-MOCCA M2020.
DOI: 10.6084/m9.figshare.14559299
title: Additional file 7 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 7. Core BEs predicted by RF-MOCCA M2020.
DOI: 10.6084/m9.figshare.14559293.v1
title: Additional file 5 of MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

description: Additional file 5. Candidate BEs predicted by RF-MOCCA M2020.
DOI: 10.6084/m9.figshare.14559286.v1
title: Additional file 10 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 10: Table S46. The common/specific enriched pathways for BRCA and MBCA using ReactomeFIVIz (FDR
DOI: 10.6084/m9.figshare.14559289
title: Additional file 1 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 1: Table S1. The original mutation files for primary breast tumors
DOI: 10.6084/m9.figshare.14559289.v1
title: Additional file 1 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 1: Table S1. The original mutation files for primary breast tumors
DOI: 10.6084/m9.figshare.14559286
title: Additional file 10 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 10: Table S46. The common/specific enriched pathways for BRCA and MBCA using ReactomeFIVIz (FDR
DOI: 10.6084/m9.figshare.14559292
title: Additional file 2 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 2: Table S2 and S3. The original mutation files for metastasis breast tumors
DOI: 10.6084/m9.figshare.14559301
title: Additional file 4 of EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

description: Additional file 4: Table S4–S7. List of positive/negative gene set for BRCA and MBCA. Table S8–S10. Homo_sapiens genes for BRCA and MBCA