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Items: 8

1.

A machine learning based framework to identify and classify long terminal repeat retrotransposons.

Schietgat L, Vens C, Cerri R, Fischer CN, Costa E, Ramon J, Carareto CMA, Blockeel H.

PLoS Comput Biol. 2018 Apr 23;14(4):e1006097. doi: 10.1371/journal.pcbi.1006097. eCollection 2018 Apr.

2.

On the Search for Retrotransposons: Alternative Protocols to Obtain Sequences to Learn Profile Hidden Markov Models.

Fischer CN, Campos VA, Barella VH.

J Comput Biol. 2018 May;25(5):517-527. doi: 10.1089/cmb.2017.0219. Epub 2018 Jan 3.

PMID:
29297699
3.

Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior.

Fischer CN, Trautman EP, Crawford JM, Stabb EV, Handelsman J, Broderick NA.

Elife. 2017 Jan 9;6. pii: e18855. doi: 10.7554/eLife.18855.

4.

Learning HMMs for nucleotide sequences from amino acid alignments.

Fischer CN, Carareto CM, dos Santos RA, Cerri R, Costa E, Schietgat L, Vens C.

Bioinformatics. 2015 Jun 1;31(11):1836-8. doi: 10.1093/bioinformatics/btv054. Epub 2015 Jan 31.

PMID:
25638811
5.

Frequent replenishment sustains the beneficial microbiome of Drosophila melanogaster.

Blum JE, Fischer CN, Miles J, Handelsman J.

MBio. 2013 Nov 5;4(6):e00860-13. doi: 10.1128/mBio.00860-13.

7.

Exploring the diversity of the microbial world.

Fischer CN.

Yale J Biol Med. 2011 Mar;84(1):55-8.

8.

Tetryl exposure; analysis of 4 years of medical experience with tetryl.

FISCHER CN, MURDOCK HD.

Ind Med Surg. 1946 Jul;15:428. No abstract available.

PMID:
20989786

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