Do machine learning predictors of microbial phenotype from genotype identify causal variants - TIB AV-Portal

Do machine learning predictors of microbial phenotype from genotype identify causal variants

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Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Mahmoudisaber, Morteza

Formal Metadata

Title

Do machine learning predictors of microbial phenotype from genotype identify causal variants

Title of Series

Mathematics and Statistics of Genomic Epidemiology

Number of Parts

38

Author

Mahmoudisaber, Morteza

License

CC Attribution - NonCommercial - NoDerivatives 4.0 International:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.

Identifiers

10.14288/1.0397451 (DOI)

Publisher

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Release Date

Language

Content Metadata

Subject Area	Life Sciences Mathematics
Genre	Lecture

Series

Mathematics and Statistics of Genomic Epidemiology9 / 38

1

22:01

Bayesian community modelling of the microbiome

2

05:12

Combining sequencing technologies and data mining to study (mega)plasmid families

3

05:26

Considerations for estimating Rt in Alberta, Canada

4

04:59

Inference of Multiscale Selection using a Continuous Type Birth Death Model

5

03:29

Using phylodynamics and decision trees to estimate the effects of mutation on viral fitness

6

05:42

Case fatality imputation using machine learning

7

03:45

Benchmarking computational doublet-detection methods for single-cell RNA sequencing data

8

20:12

SARS-CoV-2 Phylodynamic inference - with and without phylogenies

9

05:50

Do machine learning predictors of microbial phenotype from genotype identify causal variants

10

04:21

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

11

04:59

Pop-PUNK-web: In-browser genome sketching for secure real-time genomic epidemiology

12

19:53

The evolution of multi-strain bacterial populations

13

03:19

ggCaller: a gene caller for bacterial pangenome graphs

14

04:28

Deep clustering of bacterial tree images

15

15:11

Mapping the shared evolutionary trajectories of resistance genes and other (possibly!) bad actors

16

19:16

How different is the flu you transmit from the flu you received

17

20:12

High-resolution characterization of global Salmonella subpopulations

18

21:22

Genomic tracking of SARS-CoV-2 evolution and spread

19

19:57

Decomposing the sources of SARS-CoV-2 fitness variation using phylodynamics

20

19:54

Genomic epidemiology of SARS-CoV-2 in Quebec

21

24:01

Large-scale phylogeography of hCoV-19

22

20:03

Estimating the age groups that sustain resurging COVID-19 epidemics in the United States

23

20:21

Phylodynamics and genealogy-valued Markov processes

24

22:23

Bayesian inference of parameters in transmission models

25

19:37

Additive uncorrelated relaxed clock models

26

22:03

Bayesian modeling and data integration in infectious disease phylodynamics

27

22:05

Hierarchical population genomic structure of bacterial pathogens in EnteroBase

28

20:25

Estimating population-level HIV transmission dynamics from deep-sequence data

29

20:40

Phylogenetic inference with distribution continuum

30

24:29

Improving the interpretability of k-mer-based signatures for antibiotic resistance prediction

31

22:04

Hardware-accelerated genome sketching enables real-time genomic epidemiology of pathogens

32

22:40

mbImpute: an accurate and robust imputation method for microbiome data

33

21:34

Adaptation to the cervical environment is associated with increased antibiotic susceptibility in Neisseria gonorrhoeae

34

17:48

Population genomics of A. baumannii reveals a licentious and highly mobile resistome

35

19:28

Increased power from conditional bacterial genome-wide association identifies macrolide resistance mutations in Neisseria gonorrhoeae

36

18:11

Synthetic biology approaches to suppression of antibiotic resistance

37

19:39

Identifying prognostic pairwise relationships among bacterial species in microbiome studies

38

21:25

Interpretable machine learning methods for predicting drug resistance

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