Joseph Esquibel, Deborah L Rook, Sondra M LoRe, John H Starnes, Jillian M Miller, Jennifer G Buntz, Alys Hugo, Christianne B Nieuwsma, Heather Seitz, Ahrash Bissell, Louis Gross, Stacey Kiser, Suzanne Lenhart, Michael A Mills, Claudia Neuhauser, Irene Corriette, Sarah Prescott, Kristin P Jenkins, Vedham Karpakakunjaram
Quantitative Biology at Community Colleges, a Network of Biology and Mathematics Faculty Focused on Improving Numerical and Quantitative Skills of Students
CBE life sciences education
enabled explosion data substantial increase computational power deep learning transformed fields computer vision natural language processing nlp become successful method applied many transcriptomic analysis tasks core advantage deep learning inherent capability incorporate feature computation within machine learning models results comprehensive machinereadable representation sequences facilitating downstream classification clustering tasks compared machine translation problems nlp feature embedding particularly challenging transcriptomic studies sequences string thousands nucleotides length make longterm dependencies features different parts sequence even difficult capture highlights need nucleotide sequence embedding methods capable learning input sequence features implicitly introduce ntembd deep learning embedding tool captures dependencies different features sequences learns latent representation given nucleotide sequences provide two sample use cases describing learned rna features used downstream analysis first use case demonstrates ntembds utility classifying coding noncoding rna benchmarked existing tools second one explores utility learned representations identifying adapter sequences nanopore rnaseq reads tool well trained models freely available github httpsgithubcombcgscntembd