# pos

id: 10_1101-2021_01_06_425550
pos: NNP
token:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/HG00733_SNP_set

lemma:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/HG00733_SNP_set


id: 10_1101-2021_01_06_425550
pos: NNP
token:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WGS

lemma:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WGS


id: 10_1101-2021_01_06_425550
pos: NNP
token:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WES

lemma:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WES


id: 10_1101-2021_01_06_425550
pos: NNP
token:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_missing2ref

lemma:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_missing2ref


id: 10_1101-2021_01_06_425550
pos: NNP
token:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_GRCh37_missing2ref

lemma:
  https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_GRCh37_missing2ref


id: 10_1101-2020_08_13_249839
pos: NNP
token:
  https://www.codecogs.com/eqnedit.php?latex=b_s%3D%5Cdfrac%7B1%7D%7Bcov_s%5Ctimes%20L_g%7D%5Csum_%7Be%7DW_%7Bs%2Ce%7D#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=b_s%3D%5Cdfrac%7B1%7D%7Bcov_s%5Ctimes%20L_g%7D%5Csum_%7Be%7DW_%7Bs%2Ce%7D#0


id: 10_1101-2020_08_13_249839
pos: NN
token:
  https://www.codecogs.com/eqnedit.php?latex=L_%7Bg%2Cl%7D%3D%5Cdfrac%7B1%7D%7BH%7D%5Csum_%7Bh%3D1%7D%5E%7BH%7DL_%7Bg%2Ch%2Cl%7D#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=l_%7bg%2cl%7d%3d%5cdfrac%7b1%7d%7bh%7d%5csum_%7bh%3d1%7d%5e%7bh%7dl_%7bg%2ch%2cl%7d#0


id: 10_1101-2020_08_13_249839
pos: NN
token:
  https://www.codecogs.com/eqnedit.php?latex=L_%7Bs%2Cl%7D%3D%5Cdfrac%7Bkms_%7Bs%2Cl%7D%7D%7Bcov_s%5Ctimes%20b_%7B%5Chat%7Bs%7D%7D%7D#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=l_%7bs%2cl%7d%3d%5cdfrac%7bkms_%7bs%2cl%7d%7d%7bcov_s%5ctimes%20b_%7b%5chat%7bs%7d%7d%7d#0


id: 10_1101-2020_08_13_249839
pos: NNP
token:
  https://www.codecogs.com/eqnedit.php?latex=%5Chat%7Bs%7D%3D%5Coperatorname*%7Bargmax%7D_%7Bs%27%2C%20s%27%5Cin%20GT%2C%20s%27%5Cneq%20s%7D%20r%5E2(RD_%7Bs%27%7D%2CRD_s)#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=%5Chat%7Bs%7D%3D%5Coperatorname*%7Bargmax%7D_%7Bs%27%2C%20s%27%5Cin%20GT%2C%20s%27%5Cneq%20s%7D%20r%5E2(RD_%7Bs%27%7D%2CRD_s)#0


id: 10_1101-2020_08_13_249839
pos: NN
token:
  https://www.codecogs.com/eqnedit.php?latex=(read%5C_%20length-kmer%5C_size%2B1)%5Ctimes%200.9%3D117#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=(read%5c_%20length-kmer%5c_size%2b1)%5ctimes%200.9%3d117#0


id: 10_1101-2020_08_13_249839
pos: NNP
token:
  https://www.codecogs.com/eqnedit.php?latex=V_%7BST%7D%5Bi%5D%3Dmax(0%2C%20%5Cfrac%7Bvar_%7Ball%7D-%5Cfrac%7B1%7D%7Bn%7D%5Csum_%7Bp%5Cin%20P%7D%7Bvar_p%5Ctimes%20n_p%7D%7D%7Bvar_%7Ball%7D%7D)#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=V_%7BST%7D%5Bi%5D%3Dmax(0%2C%20%5Cfrac%7Bvar_%7Ball%7D-%5Cfrac%7B1%7D%7Bn%7D%5Csum_%7Bp%5Cin%20P%7D%7Bvar_p%5Ctimes%20n_p%7D%7D%7Bvar_%7Ball%7D%7D)#0


id: 10_1101-2020_08_13_249839
pos: NNP
token:
  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/hd_genotype_chip/ALL.chip.omni_broa

lemma:
  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/hd_genotype_chip/ALL.chip.omni_broa


id: 10_1101-2020_12_24_424332
pos: NNP
token:
  https://virological.org/t/preliminary-genomic-characterisation-of-an-emergent-sars-cov-2-lineage-in-the-uk-defined-by-a-novel-set-of-spike-mutations/563

lemma:
  https://virological.org/t/preliminary-genomic-characterisation-of-an-emergent-sars-cov-2-lineage-in-the-uk-defined-by-a-novel-set-of-spike-mutations/563


# ent

id: 10_1101-2021_01_08_425967
type: FAC
entity:
  http://f1000.com/work/bibliography/8561657 http://f1000.com/work/bibliography/8561657 http://f1000.com/work/bibliography/8561657 http://f1000.com/work/bibliography/8561657


id: 10_1101-2020_04_17_043323
type: PERSON
entity:
  https://paperpile.com/c/M1ZZqK/meu9 https://paperpile.com/c/M1ZZqK/Q3l3 https://paperpile.com/c/M1ZZqK/C6cd https://paperpile.com/c/M1ZZqK/C6cd


id: 10_1101-2020_04_17_043323
type: PERSON
entity:
  http://paperpile.com/b/M1ZZqK/meu9 http://paperpile.com/b/M1ZZqK/meu9 http://paperpile.com/b/M1ZZqK/meu9 http://paperpile.com/b/M1ZZqK/meu9 http://paperpile.com/b/M1ZZqK/meu9 http://paperpile.com/b/M1ZZqK/meu9


id: 10_1101-2020_04_17_043323
type: ORG
entity:
  http://paperpile.com/b/M1ZZqK/0lD1 http://paperpile.com/b/M1ZZqK/0lD1 http://paperpile.com/b/M1ZZqK/0lD1 http://paperpile.com/b/M1ZZqK/0lD1 http://paperpile.com/b/M1ZZqK/0lD1 http://paperpile.com/b/M1ZZqK/0lD1


id: 10_1101-2021_01_08_425952
type: WORK_OF_ART
entity:
  An R Package for the Analysis of Drug Prescriptions in Electronic Health Care Records JSS Journal of Statistical Software MMMMMM YYYY


id: 10_1101-2021_01_08_425952
type: MONEY
entity:
  https://github.com/acnash/rdrugtrajectory https://github.com/acnash/rdrugtrajectory https://github.com/acnash/rdrugtrajectory


id: 10_1101-2021_01_08_425952
type: WORK_OF_ART
entity:
  Persistence and Switching Patterns of Oral Migraine Prophylactic Medications Among Patients with Chronic Migraine: A Retrospective Claims Analysis


id: 10_1101-2021_01_08_425952
type: PERSON
entity:
  http://dx.doi.org/10.18637/jss.v000.i00 https://doi.org/10.1101/2021.01.08.425952 http://creativecommons.org/licenses/by-nd/4.0/


id: 10_1101-2021_01_08_425379
type: PERSON
entity:
  http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1101/2021.01.08.425379 http://creativecommons.org/licenses/by/4.0/ 2


id: 10_1101-2021_01_08_425379
type: WORK_OF_ART
entity:
  Y613 TSGRCYHLRHKVLPRWVWEKVPDPANSSSGQPHMEQVPEAQPLGDLPILEVEEMEPPPVMESS QPAQATAPLDSGYEKHFLPTPEELGLLGPPRPQVLA* Fig


id: 10_1101-2021_01_07_425794
type: PERCENT
entity:
  0.60 0.65 0.70 0.75 0.80 0.60 0.65 0.70 0.75 0.80 0.60 0.65 0.70 0.75 0.80 0.60 0.65 0.70 0.75 0.80 0.60 0.65


id: 10_1101-2021_01_08_425885
type: QUANTITY
entity:
  http://bethgelab.org/datasets/vanhateren https://support.10xgenomics.com/single-cell-gene-expression/datasets/4.0.0/Parent_NGSC3_DI_PBMC https://support.10xgenomics.com/single-cell-gene-expression/datasets/4.0.0/Parent_NGSC3_DI_PBMC https://github.com/MarioniLab/EmbryoTimecourse2018


id: 10_1101-2021_01_08_425885
type: ORG
entity:
  Non-Trivial Intrinsic Curvature Discussion Methods Differential Geometry of Theoretical Manifolds Details


id: 10_1101-2021_01_08_425885
type: ORG
entity:
  Data Details of Extrinsic Approach to Curvature Estimation Quadratic Regression on Local Neighborhoods of Data Selecting Local Neighborhoods for Regression Goodness-of-Fit Test for Quadratic Regression Standard Error


id: 10_1101-2021_01_08_425885
type: ORG
entity:
  Bias of Scalar Curvature Estimate Note on Length Scales Details of Toy Manifold Curvature Computations Analytical Forms


id: 10_1101-2021_01_05_425414
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.05.425414 http://creativecommons.org/licenses/by-nc-nd/4.0/


id: 10_1101-2021_01_05_425414
type: DATE
entity:
  https://stm.sciencemag.org/content/10/466/eaat4921 https://stm.sciencemag.org/content/10/466/eaat4921


id: 10_1101-2021_01_05_425414
type: PERSON
entity:
  https://stm.sciencemag.org/content/10/466/eaat4921.full.pdf https://doi.org/10.1101/2021.01.05.425414 http://creativecommons.org/licenses/by-nc-nd/4.0/


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Ejection Fraction: Misunderstood and Over-rated (Changing the Paradigm in Categorizing Heart Failure


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Deep Learning Techniques for Automatic MRI Cardiac Multi- Structures Segmentation and Diagnosis: Is the Problem Solved


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart: A Statement for Healthcare Professionals From the .CC-BY-NC-ND 4.0 International


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest


id: 10_1101-2021_01_05_425266
type: WORK_OF_ART
entity:
  Evaluation of Left Ventricular Diastolic Function by Global Strain Rate Imaging in Patients with Obstructive Hypertrophic Cardiomyopathy:


id: 10_1101-2021_01_06_425550
type: PERSON
entity:
  google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/ cohort_dv_glnexus_opt/v3_missing2ref


id: 10_1101-2021_01_06_425550
type: PERSON
entity:
  google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/ cohort_dv_glnexus_opt/v3_GRCh37_missing2ref


id: 10_1101-2020_12_26_424429
type: PERSON
entity:
  preprint https://doi.org/10.1101/2020.12.26.424429 http://creativecommons.org/licenses/by-nc-nd/4.0/ 2


id: 10_1101-2020_12_26_424429
type: PERSON
entity:
  preprint https://doi.org/10.1101/2020.12.26.424429 http://creativecommons.org/licenses/by-nc-nd/4.0/ Figure


id: 10_1101-2020_12_26_424429
type: PERSON
entity:
  preprint https://doi.org/10.1101/2020.12.26.424429 http://creativecommons.org/licenses/by-nc-nd/4.0/


id: 10_1101-2020_12_26_424429
type: EVENT
entity:
  Comparison Time Analysis Geographical Analysis Clade Analysis Data Description PCR Primer Comparison Time Analysis Methods Geographical Analysis Methods Clade Analysis Methods References


id: 10_1101-2021_01_07_425716
type: ORG
entity:
  Computational Biophysics, Faculty of Biology and Center for Medical Biotechnology, University of Duisburg-Essen


id: 10_1101-2021_01_07_425716
type: PERCENT
entity:
  0.06 0.05 0.00 0.00 0.01 0.00 0.03 1.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 0.04 0.11 0.01 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.03 0.02 0.02 0.03 0.01 0.35


id: 10_1101-2021_01_07_425697
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.07.425697 http://creativecommons.org/licenses/by/4.0/ features


id: 10_1101-2021_01_07_425697
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.07.425697 http://creativecommons.org/licenses/by/4.0/ mechanism


id: 10_1101-2021_01_07_425697
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.07.425697 http://creativecommons.org/licenses/by/4.0/ sites


id: 10_1101-2021_01_05_425384
type: PERSON
entity:
  http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://paperpile.com/b/m1KDL1/wfRst http://dx.doi.org/10.1016/j.eururo.2014.11.025


id: 10_1101-2020_05_22_110247
type: FAC
entity:
  https://figshare.com/projects/Integrated_CRISPR/78252 https://github.com/DepMap-Analytics/IntegratedCRISPR


id: 10_1101-2020_05_22_110247
type: ORG
entity:
  http://paperpile.com/b/BNwyax/VOtGa http://paperpile.com/b/BNwyax/VOtGa http://paperpile.com/b/BNwyax/VOtGa http://paperpile.com/b/BNwyax/VOtGa http://paperpile.com/b/BNwyax/VOtGa


id: 10_1101-2020_05_22_110247
type: PERSON
entity:
  http://paperpile.com/b/BNwyax/DcTjJ http://paperpile.com/b/BNwyax/DcTjJ http://paperpile.com/b/BNwyax/DcTjJ


id: 10_1101-2020_05_22_110247
type: PERSON
entity:
  http://paperpile.com/b/BNwyax/iQbeE http://paperpile.com/b/BNwyax/iQbeE http://paperpile.com/b/BNwyax/iQbeE http://paperpile.com/b/BNwyax/iQbeE http://paperpile.com/b/BNwyax/iQbeE http://paperpile.com/b/BNwyax/iQbeE


id: 10_1101-2020_05_22_110247
type: ORG
entity:
  http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/ezH2 http://paperpile.com/b/BNwyax/RXWN http://paperpile.com/b/BNwyax/RXWN http://paperpile.com/b/BNwyax/RXWN http://paperpile.com/b/BNwyax/RXWN http://paperpile.com/b/BNwyax/RXWN http://paperpile.com/b/BNwyax/RXWN


id: 10_1101-2020_05_22_110247
type: PERSON
entity:
  http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/z554A http://paperpile.com/b/BNwyax/KXhhL


id: 10_1101-2020_05_22_110247
type: MONEY
entity:
  http://paperpile.com/b/BNwyax/Ztmd http://paperpile.com/b/BNwyax/Ztmd http://paperpile.com/b/BNwyax/Ztmd http://paperpile.com/b/BNwyax/Ztmd


id: 10_1101-2021_01_04_425335
type: WORK_OF_ART
entity:
  An R Package for Nonparametric Clustering Based on Local Shrinking, Journal of Statistical Software 2010;33:16


id: 10_1101-2020_08_13_249839
type: PERSON
entity:
  https://www.codecogs.com/eqnedit.php?latex=kms%2F(cov_s%5Ctimes%20%5Chat%7Bb%7D)#0 https://www.codecogs.com/eqnedit.php?latex=cov_s#0


id: 10_1101-2020_08_13_249839
type: MONEY
entity:
  https://paperpile.com/c/H8ctd0/4q2kL+jzBJy https://paperpile.com/c/H8ctd0/lyx1d https://www.codecogs.com/eqnedit.php?latex=kms%2Fcov#0


id: 10_1101-2020_08_13_249839
type: ORG
entity:
  https://paperpile.com/c/H8ctd0/gwpe https://doi.org/10.1101/2020.08.13.249839 http://creativecommons.org/licenses/by-nc-nd/4.0/ Discussion


id: 10_1101-2020_08_13_249839
type: PERSON
entity:
  http://paperpile.com/b/H8ctd0/JmTrf http://paperpile.com/b/H8ctd0/wQpB6 http://paperpile.com/b/H8ctd0/wQpB6 http://paperpile.com/b/H8ctd0/wQpB6 http://paperpile.com/b/H8ctd0/wQpB6 http://paperpile.com/b/H8ctd0/wQpB6 http://paperpile.com/b/H8ctd0/R2BY9


id: 10_1101-2020_08_13_249839
type: EVENT
entity:
  http://paperpile.com/b/H8ctd0/haw16 http://paperpile.com/b/H8ctd0/haw16 http://paperpile.com/b/H8ctd0/haw16 http://paperpile.com/b/H8ctd0/haw16 http://paperpile.com/b/H8ctd0/haw16


id: 10_1101-2020_08_13_249839
type: PERSON
entity:
  http://paperpile.com/b/H8ctd0/cRk7v http://paperpile.com/b/H8ctd0/cRk7v http://paperpile.com/b/H8ctd0/cRk7v http://paperpile.com/b/H8ctd0/cRk7v http://paperpile.com/b/H8ctd0/cRk7v http://dx.doi.org/10.1101/563866 http://paperpile.com/b/H8ctd0/cRk7v


id: 10_1101-2020_08_13_249839
type: WORK_OF_ART
entity:
  http://paperpile.com/b/H8ctd0/oc37W http://paperpile.com/b/H8ctd0/oc37W http://paperpile.com/b/H8ctd0/oc37W http://paperpile.com/b/H8ctd0/oc37W http://paperpile.com/b/H8ctd0/8Gyl


id: 10_1101-2021_01_05_425508
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.05.425508 http://creativecommons.org/licenses/by-nc/4.0/ 10


id: 10_1101-2021_01_05_425508
type: PERSON
entity:
  preprint https://doi.org/10.1101/2021.01.05.425508 http://creativecommons.org/licenses/by-nc/4.0/ 14


id: 10_1101-436634
type: ORG
entity:
  the ‘Burroughs Wellcome Fund Institutional Program Unifying Population and Laboratory Based Sciences Award’


# url

id: 10_1101-2021_01_06_425550
domain: console.cloud.google.com
url:
  http://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/HG00733_SNP_set


id: 10_1101-2021_01_06_425550
domain: console.cloud.google.com
url:
  http://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WGS


id: 10_1101-2021_01_06_425550
domain: console.cloud.google.com
url:
  http://console.cloud.google.com/storage/browser/brain-genomics-public/research/allele_frequency/pretrained_model_WES


id: 10_1101-2021_01_06_425550
domain: console.cloud.google.com
url:
  http://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_missing2ref


id: 10_1101-2021_01_06_425550
domain: console.cloud.google.com
url:
  http://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP/cohort_dv_glnexus_opt/v3_GRCh37_missing2ref


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=b_s%3D%5Cdfrac%7B1%7D%7Bcov_s%5Ctimes%20L_g%7D%5Csum_%7Be%7DW_%7Bs%2Ce%7D#0


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=L_%7Bg%2Cl%7D%3D%5Cdfrac%7B1%7D%7BH%7D%5Csum_%7Bh%3D1%7D%5E%7BH%7DL_%7Bg%2Ch%2Cl%7D#0


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=L_%7Bs%2Cl%7D%3D%5Cdfrac%7Bkms_%7Bs%2Cl%7D%7D%7Bcov_s%5Ctimes%20b_%7B%5Chat%7Bs%7D%7D%7D#0


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=%5Chat%7Bs%7D%3D%5Coperatorname*%7Bargmax%7D_%7Bs%27%2C%20s%27%5Cin%20GT%2C%20s%27%5Cneq%20s%7D%20r%5E2(RD_%7Bs%27%7D%2CRD_s)#0


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=(read%5C_%20length-kmer%5C_size%2B1)%5Ctimes%200.9%3D117#0


id: 10_1101-2020_08_13_249839
domain: www.codecogs.com
url:
  http://www.codecogs.com/eqnedit.php?latex=V_%7BST%7D%5Bi%5D%3Dmax(0%2C%20%5Cfrac%7Bvar_%7Ball%7D-%5Cfrac%7B1%7D%7Bn%7D%5Csum_%7Bp%5Cin%20P%7D%7Bvar_p%5Ctimes%20n_p%7D%7D%7Bvar_%7Ball%7D%7D)#0


id: 10_1101-2020_12_24_424332
domain: virological.org
url:
  http://virological.org/t/preliminary-genomic-characterisation-of-an-emergent-sars-cov-2-lineage-in-the-uk-defined-by-a-novel-set-of-spike-mutations/563


# bib

id: 10_1101-2021_01_08_425887
title: Auto-CORPus: Automated and Consistent Outputs from Research Publications
author: Hu, Yan
date: 2021
txt: ./txt/10_1101-2021_01_08_425887.txt
cache: ./cache/10_1101-2021_01_08_425887.pdf
extension: .pdf
mime: application/pdf
summary:
  the same structured model, so that these can be used as input to rule-based or deep learning algorithms for data extraction. example, at this point in this article the main headers are ''abstract'' followed by ''introduction'' and ''materials and methods'' that could make up a digraph. We use this process to evaluate new potential synonyms for existing terms and identify abstract → introduction → materials → results → discussion → conclusion → acknowledgements → footnotes section → references. Based on the digraph, we then assigned data and data description to be synonyms of the materials section, and participants From the analysis of ego-networks four new potential categories were identified: disclosure, graphical abstract, highlights and participants. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications.


id: 10_1101-2021_01_08_425897
author: Soleymanjahi, Saeed
date: 2021
title:
  APOBEC1 mediated C-to-U RNA editing: target sequence and trans-acting factor contribution to 177 RNA editing events in 119 murine transcripts in-vivo


id: 10_1101-2021_01_08_425855
title: DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.
author: Wu, Canbiao
date: 2021
txt: ./txt/10_1101-2021_01_08_425855.txt
cache: ./cache/10_1101-2021_01_08_425855.pdf
extension: .pdf
mime: application/pdf
summary:
  DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites. deep learning model DeepHBV to predict HBV integration sites by learning local learning model DeepHBV to predict HBV integration sites by learning local genomic DeepHBV effectively predicts HBV integration sites by adding genomic features. mixed HBV integration sequences, positive genome feature samples, and randomly peaks and DeepHBV with HBV integration sequences + TCGA Pan Cancer peaks) on model trained with HBV integrated sequences + TCGA Pan Cancer showed an performed better compared with DeepHBV model with HBV integration sequences + HBV integration sites + TCGA Pan Cancer, a cluster of attention weights much output of DeepHBV with HBV integration sites plus TCGA Pan Cancer showed the of DeepHBV with HBV integration sequences + TCGA Pan Cancer showed strong DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) 


id: 10_1101-2021_01_08_425976
author: Ahuja, Yuri
date: 2021
txt: ./txt/10_1101-2021_01_08_425976.txt
cache: ./cache/10_1101-2021_01_08_425976.pdf
extension: .pdf
mime: application/xhtml+xml
summary:
  bioRxiv.org the preprint server for Biology Skip to main content Home Submit ALERTS / RSS Search for this keyword Advanced Search Subject Areas All Articles Animal Behavior and Cognition Biochemistry Bioengineering Bioinformatics Biophysics Cancer Biology Cell Biology Clinical Trials Developmental Biology Ecology Epidemiology Evolutionary Biology Genetics Genomics Immunology Microbiology Molecular Biology Neuroscience Paleontology Pathology Pharmacology and Toxicology Physiology Plant Biology Scientific Communication and Education Synthetic Biology Systems Biology Zoology View by Month

title:
  Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data


id: 10_1101-2021_01_08_425918
author: Yogodzinski, Christopher H
date: 2021
title:
  A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.


id: 10_1101-2021_01_08_425952
author: Nash, Anthony
date: 2021
title:
  rdrugtrajectory: An R Package for the Analysis of Drug Prescriptions in Electronic Health Care Records


id: 10_1101-2021_01_08_426008
author: Pipes, Lenore
date: 2021
txt: ./txt/10_1101-2021_01_08_426008.txt
cache: ./cache/10_1101-2021_01_08_426008.pdf
extension: .pdf
mime: application/pdf
summary:
  AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster At low identities, these methods produce uneven clusters where the majority of sequences are are no clustering methods that can accurately cluster large taxonomically divergent metabarcoding reference databases such as databases (Schoch et al., 2020), there is a need for new computationally efficient methods that can cluster divergent sequences. To cluster divergent sequences, we developed AncestralClust clustering methods: UCLUST (Edgar, 2010), meshclust2 (James dataset against UCLUST because it is the most widely used clustering program and it performs better than CD-HIT on low identity We developed a phylogenetic-based clustering method, AncestralClust, specifically to cluster divergent metabarcode sequences. Comparisons of clustering methods using 13,043 COI sequences from 11 different species.

title:
  AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees


id: 10_1101-2021_01_08_425967
author: Camacho-Hernández, Diego A.
date: 2021
title:
  Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data


id: 10_1101-2021_01_08_425379
author: Wilmes, Stephan
date: 2021
title:
  Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders


id: 10_1101-2020_04_17_043323
author: Waschke, Johannes
date: 2021
txt: ./txt/10_1101-2020_04_17_043323.txt
cache: ./cache/10_1101-2020_04_17_043323.pdf
extension: .pdf
mime: application/pdf
summary:
  linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser linus: Conveniently explore, share, and present large-scale biological trajectory data In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating We provide a python script that reads trajectory data and enriches them with additional features, such as edge bundling or custom axes and generates an interactive web-based visualisation that can be shared offline from diffusion MRI imaging (Liu et al., 2020), or tracking data such as cell trajectories or animal trails (Romero-Ferrero et visualisation tool linus, making it easier to explore 3D trajectory data from any device without a local installation of Creating a visualisation package with linus is done in a few simple steps (Fig. 1a): The user imports trajectory data from a Figure 1 Browser-based exploration and sharing of trajectory visualizations with linus.

title:
  <em>linus</em>: Conveniently explore, share, and present large-scale biological trajectory data from a web browser


id: 10_1101-2021_01_06_425560
title: Review and performance evaluation of trait-based between-community dissimilarity measures
author: Lengyel, Attila
date: 2021
txt: ./txt/10_1101-2021_01_06_425560.txt
cache: ./cache/10_1101-2021_01_06_425560.pdf
extension: .pdf
mime: application/pdf
summary:
  Review and performance evaluation of trait-based between-community dissimilarity measures 2 Review and performance evaluation of trait-based between-community dissimilarity measures 2 2. In this paper we reviewed the trait-based dissimilarity indices available in the 16 dissimilarities calculated by different indices correlate with environmental distances. beta diversity, dissimilarity index, distance metric, community ecology, functional traits 39 including several families of trait-based dissimilarity indices. FDissim indices incorporate trait information into the calculation of dissimilarity in different 162 Indices following this approach represent each community with a typical trait value, and 185 2005) or trait-based dissimilarity of species (Lepš 220 of the similarity indices for presence/absence data disregarding species properties, while the 281 ordinariness values in the species-based (dis-)similarity indices. Ricotta & Pavoine (2015) introduced a new family of trait-based similarity measures called 331 For species-based analyses, Ricotta & Podani (2017) suggested a general formula of distance 336 compared how strongly the dissimilarity indices correlate with the environmental distance 515 


id: 10_1101-2021_01_07_425637
title: A mammalian methylation array for profiling methylation levels at conserved sequences
author: Arneson, Adriana
date: 2021
txt: ./txt/10_1101-2021_01_07_425637.txt
cache: ./cache/10_1101-2021_01_07_425637.pdf
extension: .pdf
mime: application/pdf
summary:
  characterize the CpGs on the mammalian methylation array with various genomic annotations. Array probes are sequences of length 50bp flanking a target CpG based on the human reference We added probes targeting 1986 CpGs to the mammalian methylation array based on All 37488 CpGs profiled on the mammalian methylation array apply to humans, but only a CpGs on the mammalian array cover 6871 human and 5659 mouse genes when each DNA methylation samples for three species: human (n=10 arrays), mouse (n=20), and rat (n=15), synthetic DNA data from 3 species: human (n=10 mammalian arrays), mouse (n=20), and rat CpG and gene coverage of probes on the mammalian methylation array across CpG island and chromatin state analysis of mammalian methylation probes. probes targeting the same CpG that can also be found on the human EPIC array that were not mammalian methylation array to the human (hg19) and mouse (mm10) genome using QUASR 


id: 10_1101-2021_01_07_425782
author: Lu, Tianyu
date: 2021
title:
  dynUGENE: an R package for uncertainty-aware gene regulatory network inference, simulation, and visualization


id: 10_1101-2021_01_07_425794
title: Impact of gene annotation choice on the quantification of RNA-seq data
author: Chisanga, David
date: 2021
txt: ./txt/10_1101-2021_01_07_425794.txt
cache: ./cache/10_1101-2021_01_07_425794.pdf
extension: .pdf
mime: application/pdf
summary:
  Despite the importance of gene annotations in RNA-seq data analysis, very little research has been conducted to examine how differences in annotations impact on gene compared the effect of human genome annotations from popular databases including Ensembl, GENCODE and RefSeq on various aspects of RNA-seq analysis and they showed gene-level expression quantification in an RNA-seq data analysis pipeline. The Ensembl, RefSeq-NCBI and RefSeqRsubread annotations were provided to featureCounts to generate read counts for genes Gene expression data generated using TaqMan RT-PCR and Illumina''s BeadChip microarray were used to validate the gene-level quantification results from the RNA-seq The Ensembl and NCBI RefSeq annotations are among the most widely used gene annotations that have been utilized for RNA-seq gene expression quantification in the field. led to a better concordance in gene expression between the RNA-seq data and the RTPCR data, compared to the use of Ensembl and RefSeq-NCBI annotations.


id: 10_1101-2021_01_07_425801
title: Fibrinolysis influences SARS-CoV-2 infection in ciliated cells
author: Hou, Yapeng
date: 2021
txt: ./txt/10_1101-2021_01_07_425801.txt
cache: ./cache/10_1101-2021_01_07_425801.pdf
extension: .pdf
mime: application/pdf
summary:
  proteases, may cleave the furin site of SARS-CoV-2 S protein and  subunits of epithelial sodium channels ( 15 TMPRSS2, and ACE2 were significantly upregulated in severe COVID-19 patients and SARS-CoV-2 infected 22 Plasmin cleaves the furin site in SARS-CoV S protein (Kam et al. lung epithelial cells and whether SARS-CoV-2 infection alters their expression at the single-cell level. severe/moderate COVID-19 patients and SARS-CoV-2 infected cell lines, mainly owning to ciliated cells. The expression levels of proteases (PLAU, FURIN, TMPRSS2, PLG), ACE2, and SCNN1G in 11 cell 88 Expression levels of PLAU, SCNN1G, and ACE2 in SARS-CoV-2 infection 93 epithelial cell lines infected with SARS-CoV-2: A549, Calu-3, and NHBE (Blanco-Melo et al. CoV-2 infection also increased the expression level of ACE2 in A549 cells (P < 0.05) (Smith et al. Our data showed that the respiratory cells co-express SARS-CoV-2 receptor, ENaC 137 Changes of proteases, ACE2, and SCNN1G in respiratory cell lines after SARS-CoV-2 


id: 10_1101-2021_01_08_425885
author: Sritharan, Duluxan
date: 2021
title:
  Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry


id: 10_1101-2020_10_26_351783
author: Badam, Tejaswi V.S.
date: 2021
title:
  A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis


id: 10_1101-2021_01_06_425569
title: Metabolite discovery through global annotation of untargeted metabolomics data
author: Chen, Li
date: 2021
txt: ./txt/10_1101-2021_01_06_425569.txt
cache: ./cache/10_1101-2021_01_06_425569.pdf
extension: .pdf
mime: application/pdf
summary:
  to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine Peak annotation occurs in a single global optimization step, based on linear programming, connected nodes matches the atom mass difference and (ii) only co-eluting peaks are connected by edges receive a positive score for MS2 spectra similarity match between the connected nodes, and With a score assigned for each potential node and edge annotation, we formulate the global network A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A final edge annotation score S( 𝑢, 𝑣, 𝑎 , 𝑏 , 𝐷 ) for choosing candidate formula 𝑎 for node u, A global network optimization approach for untargeted metabolomics data annotation NetID applies global optimization for metabolomics data annotation and metabolite A global network optimization approach for untargeted metabolomics data annotation (NetID).


id: 10_1101-2021_01_06_425550
title: Improving variant calling using population data and deep learning
author: Chen, Nae-Chyun
date: 2021
txt: ./txt/10_1101-2021_01_06_425550.txt
cache: ./cache/10_1101-2021_01_06_425550.pdf
extension: .pdf
mime: application/pdf
summary:
  We further evaluated the performance of the models using two whole-exome sequencing (WES) datasets from a recently released set of genome and exome data [23] (Figure 2). Among the populationresolved false-positive errors, more than two third (71.0%) are uncommon (allele frequency ≤ 5%) among the 1000Genomes samples, whereas there are only 11.4% uncommon variants for population-induced false positives. This observation supports the hypothesis that the population-aware model uses allele frequency to adjust its variant calls. A potential concern for population-aware variant calling models is increasing false negative rate for novel alleles. To better understand the zero-frequency variants, we called variants using the DeepVariant PacBio model with the PrecisionFDA v2 35x HG003 reads set sequenced with the We evaluate potential biases introduced by population information in variant calling by comparing population-aware models that use allele frequencies from different Despite greater overall accuracy, we note that the population-aware model underperforms on variants with zero allele frequencies in 1000Genomes.


id: 10_1101-2021_01_05_425414
author: Lakatos, Eszter
date: 2021
title:
  LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations


id: 10_1101-2020_12_26_424429
title: Analysis and Forecasting of Global RT-PCR Primers for SARS-CoV-2
author: Nayar, Gowri
date: 2021
txt: ./txt/10_1101-2020_12_26_424429.txt
cache: ./cache/10_1101-2020_12_26_424429.pdf
extension: .pdf
mime: application/pdf
summary:
  SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets with respect to the sequenced SARS-CoV-2 genomes, we can measure how the targeted proteins are mutating. primer sequences and protocols developed for six different regions – USA, Germany, China, Hong Kong, Japan, and Thailand – percent of genomes hit by each PCR test, labelled by the country and target gene region. Figure 6 shows the average number of mismatches over time, grouped by the genomes sampled The results of this study also demonstrate that each primer target develops a different number of mismatches over time The mutations that lead to mismatches between gene PCR primers and their targets reflect the sequence evolution of the consistent with the observed increasing number of mismatches over time, and shows that evolution of SARS-CoV-2 genomes is of the RdRp gene, primer target creates a disproportionate number of mismatches when compared to genomes sequenced within


id: 10_1101-2021_01_07_425716
author: Cao, Yingying
date: 2021
title:
  Comprehensive comparison of transcriptomes in SARS-CoV-2 infection: alternative entry routes and innate immune responses


id: 10_1101-2021_01_07_425697
title: Capsule network for protein ubiquitination site prediction
author: Luo, Yin
date: 2021
txt: ./txt/10_1101-2021_01_07_425697.txt
cache: ./cache/10_1101-2021_01_07_425697.pdf
extension: .pdf
mime: application/pdf
summary:
  Capsule network for protein ubiquitination site prediction Capsule network for protein ubiquitination site years, some calculation methods have been developed to predict potential ubiquitination sites. this paper, a deep learning model, "Caps-Ubi," is proposed that uses a capsule network for protein network layer are used as a feature extractor to obtain the functional domains in the protein Data of protein ubiquitination sites the amino acid sequence around the protein ubiquitination site; namely, one-of-K encoding and the performance of various window sizes in one-of-21 and amino acid continuous encoding modes. modes is the best on the capsule network: this proposed Caps-Ubi model achieved an accuracy, In this paper, a new deep learning model for predicting protein ubiquitination sites is proposed, ubiquitination sites in proteins. Large-scale prediction of protein ubiquitination sites machine learning method with substrate motifs to predict ubiquitin-conjugation site on machine learning method with substrate motifs to predict ubiquitin-conjugation site on 


id: 10_1101-2020_11_13_381475
author: Pabis, Kamil
date: 2021
title:
  Triplex and other DNA motifs show motif-specific associations with mitochondrial DNA deletions and species lifespan


id: 10_1101-2021_01_04_425315
title: Sample-wise unsupervised deconvolution of complex tissues
author: Chen, Lulu
date: 2021
txt: ./txt/10_1101-2021_01_04_425315.txt
cache: ./cache/10_1101-2021_01_04_425315.pdf
extension: .pdf
mime: application/pdf
summary:
  tool enables statistically-principled subtype-level downstream analyses, such as detecting subtypespecific differentially expressed genes (sDEG) and differential dependency networks (DDN) nuclear-norm regularized low-rank matrix factorization problem (Wang, Hoffman et al. regularization to optimize the estimation of between-sample variations in each subtype to recover sample-specific deconvolution and optimization solver used in swCAM algorithm, followed by Sample-specific deconvolution problem formulation and the assumption of hidden low-rank pattern CAM-estimated subtype-specific expression matrix serves as the initial reference 𝑺. The objective function of swCAM for sample-specific deconvolution problem and its reformulation As swCAM focuses on subtype-specific variation estimation, simulating biological variance The observations for 300 genes in 50 samples were simulated with subtype-specific expression Gene co-expressed function modules detected by WGCNA on swCAM estimated sample-specific Gene co-expressed function modules detected by WGCNA on swCAM estimated samplespecific expression for each subtype with λ=5 and δ=1 or 0.1. capacity of swCAM to estimate sample-specific signals in each subtype using simulations where 


id: 10_1101-2021_01_05_425384
author: Halilaj, Iva
date: 2021
txt: ./txt/10_1101-2021_01_05_425384.txt
cache: ./cache/10_1101-2021_01_05_425384.pdf
extension: .pdf
mime: application/pdf
summary:
  of COVID-19 patients, expediting the models'' transition from research to clinical practice. The open source website https://covid19risk.ai/ currently incorporates nine models from six an inclusive platform for predictive models related to COVID-19. supplement their judgment with patient-specific predictions from externally-validated models Keywords: Covid-19, predictive models, diagnosis, prognosis, nomogram, machine We, as researchers working on COVID-19 models, saw an urgent need for a web-based Our aim for this platform is to include validated prediction models (TRIPOD type 2b and 3) published AI prediction models related to all aspects of COVID-19, including diagnosis, COVID-19 predictive models will serve as a decision aid for doctors. Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19 Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19

title:
  Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19


id: 10_1101-2021_01_02_425006
author: Wang, Qi
date: 2021
title:
  Analysis of next- and third-generation RNA-Seq data reveals the structures of alternative transcription units in bacterial genomes


id: 10_1101-332965
author: Banerjee, Arpita
date: 2021
txt: ./txt/10_1101-332965.txt
cache: ./cache/10_1101-332965.pdf
extension: .pdf
mime: application/pdf
summary:
  protease well by making favorable interactions with important residues of the enzyme. Keywords: Vinyl sulfone inhibitors, Cryptopain-1, Cysteine protease, Molecular residues that were contacted by ligand subgroups across the enzymatic cleft, in one or The ligand subgroup-contacting residues in each complex had been mutated to Alanine; favorably interacting subsite residues (derived from Supplementary Table 1) in the Interactions: enzyme subsite residues ligand subgroups with the ligand ring systems showed highly positive ddGbind values for thiophen group in always, showed favorable interactions even when no ligand group was placed near it. with no ligand group placed near the residue, the interactions were unfavorable. around the ligand subgroups of the best-scored vinyl sulfones compounds (PubChem IDs Figure1: Illustration of the typical binding of vinyl sulfone inhibitors to cysteine protease enzymes. Figure 3: All the residues that are contacted by one or more ligands in the docked complexes of 

title:
  Identification and design of vinyl sulfone inhibitors against Cryptopain-1 – a cysteine protease from cryptosporidiosis-causing <em>Cryptosporidium parvum</em>


id: 10_1101-2021_01_05_425409
author: Dholakia, Dhwani
date: 2021
title:
  HLA-SPREAD: A comprehensive resource for HLA associated diseases, drug reactions and SNPs across populations


id: 10_1101-2021_01_05_425508
author: Iwasaki, Yuki
date: 2021
title:
  Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes


id: 10_1101-2021_01_04_425335
author: Ji, Guoli
date: 2021
title:
  Learning association for single-cell transcriptomics by integrating profiling of gene expression and alternative polyadenylation


id: 10_1101-2020_01_29_925354
title: Interpretable detection of novel human viruses from genome sequencing data
author: Bartoszewicz, Jakub M.
date: 2021
txt: ./txt/10_1101-2020_01_29_925354.txt
cache: ./cache/10_1101-2020_01_29_925354.pdf
extension: .pdf
mime: application/pdf
summary:
  As the state-of-the art approach for the openview detection of pathogens is genome sequencing (5, 6), it learning (17) to predict host range for a small set of three wellstudied species directly from viral sequences. predicting whether a new virus can potentially infect humans. boundary separates human viruses from other DNA sequences generated the reads from the genomes of human-infecting constituting reads yields a prediction for the whole sequence. predictions from all the reads originating from a given genome In the Fig. 1 we present example filters, visualized as "maxcontrib" sequence logos based on mean partial Shapley values prediction directly from next-generation sequencing reads Three receptor-binding domains (RBDs) are colored in blue, white and red according to the predicted infectious potential of the corresponding genomic sequence. Interpretable detection of novel human viruses from genome sequencing data Interpretable detection of novel human viruses from genome sequencing data


id: 10_1101-2021_01_04_425250
title: A read count-based method to detect multiplets and their cellular origins from snATAC-seq data
author: Thibodeau, Asa
date: 2021
txt: ./txt/10_1101-2021_01_04_425250.txt
cache: ./cache/10_1101-2021_01_04_425250.pdf
extension: .pdf
mime: application/pdf
summary:
  A read count-based method to detect multiplets and their cellular origins from snATAC-seq data Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets 17 found that when snATAC-seq samples were adequately sequenced (e.g., >20k valid read pairs per cell), ATAC-52 ATAC-DoubletDetector detected heterotypic multiplets introduced in PBMC samples with high recall 126 detected multiplets were homotypic (76.7-84.3% in islets, 63-78.7% in PBMCs), with cell types being distributed 207 with respect to their cell proportions for both homotypic and heterotypic multiplet types (Fig. 5d-e, Extended Data 208 ATAC-DoubletDetector for identifying multiplets from snATAC-seq data with enough reads per nuclei, it can also 239 Fig. 2: ATAC-DoubletDetector identifies heterotypic and homotypic multiplets in human PBMC snATAC-seq data. e, The number of cells and percentage of multiplets detected by ATAC-DoubletDetector in PBMC and islet samples. Extended Data Fig. 6: ATAC-DoubletDetector detects both homotypic and heterotypic multiplets at high read depth.


id: 10_1101-2020_09_09_289074
title: Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex
author: Ortuso, Francesco
date: 2021
txt: ./txt/10_1101-2020_09_09_289074.txt
cache: ./cache/10_1101-2020_09_09_289074.pdf
extension: .pdf
mime: application/pdf
summary:
  Structural Genetics of circulating variants affecting the SARS-CoV-2 Spike / human ACE2 complex SARS-CoV-2, COVID-19, mutations, Spike, ACE2 SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these SARS-CoV-2 (the COVID-19 virus) and human cells, through the analysis of Spike/ACE2 complexes. future mutations targeting the ACE2/Spike binding and detected by sequencing SARS-CoV-2 on a We obtained structural models of the SARS-CoV-2 Spike interacting with the human ACE2 from three contributing to the interaction between Spike and ACE2, according to GBPM (see Table 1 and Fig 3 for A less frequent mutation amongst those predicted to contribute to the ACE2/Spike interaction is population and non-zero GBPM average score in the ACE2/Spike interaction models. ACE2 variants with non-zero GBPM score in the Spike interaction model.


id: 10_1101-436634
author: Cotto, Kelsy C.
date: 2021
txt: ./txt/10_1101-436634.txt
cache: ./cache/10_1101-436634.pdf
extension: .pdf
mime: application/pdf
summary:
  RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer somatic variants from genomic data with splice junctions from transcriptomic data to identify isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and GenotypeTissue Expression (GTEx) junction counts and compared our results to other tools that integrate tools, the unbiased nature of RegTools has allowed us to identify novel splice variants and identify potential cis-acting splice-relevant variants in tumors (www.regtools.org). To demonstrate the utility of RegTools in identifying potential splice-relevant variants from tumor transcriptome as described above and its associated variants based on splice junction region For our analysis, we annotated the pairs of associated variants and junctions identified by Pan-cancer analysis of 35 tumor types identifies somatic variants that alter canonical We also identify recurrent splice altering variants in genes not known to be cancer genes RegTools contains three sub-modules: "variants", "junctions", and "cis-splice-effects".

title:
  RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer


id: 10_1101-2020_08_28_271981
title: Full-length <em>de novo</em> protein structure determination from cryo-EM maps using deep learning
author: He, Jiahua
date: 2021
txt: ./txt/10_1101-2020_08_28_271981.txt
cache: ./cache/10_1101-2020_08_28_271981.pdf
extension: .pdf
mime: application/pdf
summary:
  Full-length de novo protein structure determination from cryo-EM maps using deep learning structure types were predicted by a second DenseNet. Finally, the protein sequence was aligned to the main-chain according to the predicted Cα probabilities, amino acid types, and secondary structure amino acid type, and secondary structure type for each main-chain point, the target protein sequence The second network (i.e. DenseNet B) is used to predict the amino acid type and secondary structure type of a main-chain local dense point (LDP). Figure 3 shows a comparison of the predicted Cα models for the protein chains of different lengths The authors acknowledge professor Daisuke Kihara and his students Genki Terashi and Sai Raghavendra Maddhuri Venkata Subramaniya from Purdue University for providing their datasets. A New Protocol for Atomic-Level Protein Structure Modeling and Refinement Using Low-to-Medium Resolution Cryo-EM Density Maps. Figure 8: Protein models reconstructed by DeepMM and Phenix for the Chain A of 6DW1


