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Literature DB >> 35535448

Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.

Abstract

The study of ancient genes in contemporary species is providing new insights about how organisms adapted to new environments during evolution.

Entities: Chemical

Mesh：

Year: 2022 PMID： 35535448 PMCID： PMC9171676 DOI： 10.15252/embr.202255315

Source DB: PubMed Journal: EMBO Rep ISSN： 1469-221X Impact factor: 9.071

9 in total

1. Bacterial natural transformation by highly fragmented and damaged DNA.

Authors: Søren Overballe-Petersen; Klaus Harms; Ludovic A A Orlando; J Victor Moreno Mayar; Simon Rasmussen; Tais W Dahl; Minik T Rosing; Anthony M Poole; Thomas Sicheritz-Ponten; Søren Brunak; Sabrina Inselmann; Johann de Vries; Wilfried Wackernagel; Oliver G Pybus; Rasmus Nielsen; Pål Jarle Johnsen; Kaare Magne Nielsen; Eske Willerslev
Journal: Proc Natl Acad Sci U S A Date: 2013-11-18 Impact factor: 11.205

2. Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.

Authors: Philip Hunter
Journal: EMBO Rep Date: 2022-05-10 Impact factor: 9.071

3. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares.

Authors: Matthew R Jones; L Scott Mills; Paulo Célio Alves; Colin M Callahan; Joel M Alves; Diana J R Lafferty; Francis M Jiggins; Jeffrey D Jensen; José Melo-Ferreira; Jeffrey M Good
Journal: Science Date: 2018-06-22 Impact factor: 47.728

4. The complete genome sequence of a Neanderthal from the Altai Mountains.

Authors: Kay Prüfer; Fernando Racimo; Nick Patterson; Flora Jay; Sriram Sankararaman; Susanna Sawyer; Anja Heinze; Gabriel Renaud; Peter H Sudmant; Cesare de Filippo; Heng Li; Swapan Mallick; Michael Dannemann; Qiaomei Fu; Martin Kircher; Martin Kuhlwilm; Michael Lachmann; Matthias Meyer; Matthias Ongyerth; Michael Siebauer; Christoph Theunert; Arti Tandon; Priya Moorjani; Joseph Pickrell; James C Mullikin; Samuel H Vohr; Richard E Green; Ines Hellmann; Philip L F Johnson; Hélène Blanche; Howard Cann; Jacob O Kitzman; Jay Shendure; Evan E Eichler; Ed S Lein; Trygve E Bakken; Liubov V Golovanova; Vladimir B Doronichev; Michael V Shunkov; Anatoli P Derevianko; Bence Viola; Montgomery Slatkin; David Reich; Janet Kelso; Svante Pääbo
Journal: Nature Date: 2013-12-18 Impact factor: 49.962

1 in total

1. Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.

Authors: Philip Hunter
Journal: EMBO Rep Date: 2022-05-10 Impact factor: 9.071

1 in total

Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.

1. Bacterial natural transformation by highly fragmented and damaged DNA.

2. Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.

3. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares.

4. The complete genome sequence of a Neanderthal from the Altai Mountains.

5. Adaptively introgressed Neandertal haplotype at the OAS locus functionally impacts innate immune responses in humans.

Review 6. Adaptive Introgression: An Untapped Evolutionary Mechanism for Crop Adaptation.

7. Detecting adaptive introgression in human evolution using convolutional neural networks.

8. Signatures of Archaic Adaptive Introgression in Present-Day Human Populations.

9. The Contribution of Neanderthals to Phenotypic Variation in Modern Humans.

1. Molecular archaeology and machine learning: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role: Deep learning algorithms help to uncover ancient genes in our genomes and their evolutionary role.