Difference between revisions of "Bioinformatics"
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| + | == What Came First, Cells or Viruses? == | ||
| + | [http://www.youtube.com/results?search_query=Phylogenomic+genomics+gene+dna+artificial+intelligence+deep+learning Youtube search...] | ||
| + | [http://www.google.com/search?q=Phylogenomic+genomics+gene+dna+deep+machine+learning+ML ...Google search] | ||
| + | |||
| + | * [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]] | ||
| + | * [http://www.di.ens.fr/~fbach/courses/fall2013/phyloHMM.pdf Phylogenetic Hidden Markov Models | Adam Siepel and David Haussler] | ||
| + | * [http://evolution.genetics.washington.edu/phylip/software.html Phylogeny Programs | Joe Felsenstein - University of Washington] Here are 392 phylogeny packages and 54 free web servers ... [http://evolution.genetics.washington.edu/phylip.html PHYLIP (the PHYLogeny Inference Package)] Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites and fragments, distance matrices, and discrete characters. | ||
Revision as of 09:21, 14 June 2020
Youtube search... ...Google search
- Case Studies
- COVID-19
- DIY Human CRISPR Guide | The Odin
- Machine learning spots natural selection at work in human genome | Amy Maxmen
- DeepVariant: Highly Accurate Genomes With Deep Neural Networks | Mark DePristo and Ryan Poplin, Google Brain Team
CRISPR Kit
CRISPR Explained
What Came First, Cells or Viruses?
Youtube search... ...Google search
- Markov Model (Chain, Discrete Time, Continuous Time, Hidden)
- Phylogenetic Hidden Markov Models | Adam Siepel and David Haussler
- Phylogeny Programs | Joe Felsenstein - University of Washington Here are 392 phylogeny packages and 54 free web servers ... PHYLIP (the PHYLogeny Inference Package) Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites and fragments, distance matrices, and discrete characters.
