PreDisorder: ab initio sequence-based prediction of protein disordered regions. - Xin Deng; Jesse Eickholt; Jianlin Cheng (BMC Bioinform., 2009)

NNcon: improved protein contact map prediction using 2D-recursive neural networks. - Allison N. Tegge; Zheng Wang 019; Jesse Eickholt; Jianlin Cheng (Nucleic Acids Res., 2009)

MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8. - Zheng Wang 019; Jesse Eickholt; Jianlin Cheng (Bioinform., 2010)

APOLLO: a quality assessment service for single and multiple protein models. - Zheng Wang 019; Jesse Eickholt; Jianlin Cheng (Bioinform., 2011)

DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning. - Jesse Eickholt; Xin Deng; Jianlin Cheng (BMC Bioinform., 2011)

Predicting protein residue-residue contacts using deep networks and boosting. - Jesse Eickholt; Jianlin Cheng (Bioinform., 2012)

The MULTICOM toolbox for protein structure prediction. - Jianlin Cheng; Jilong Li; Zheng Wang 019; Jesse Eickholt; Xin Deng (BMC Bioinform., 2012)

Recursive protein Modeling: a Divide and Conquer Strategy for protein Structure Prediction and its Case Study in CASP9. - Jianlin Cheng; Jesse Eickholt; Zheng Wang 019; Xin Deng (J. Bioinform. Comput. Biol., 2012)

DNdisorder: predicting protein disorder using boosting and deep networks. - Jesse Eickholt; Jianlin Cheng (BMC Bioinform., 2013)

A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. - Jesse Eickholt; Jianlin Cheng (BMC Bioinform., 2013)

Adopting the MapReduce framework to pre-train 1-D and 2-D protein structure predictors with large protein datasets. - Jesse Eickholt; Suman Karki (BIBM, 2014)

Adding computing theory to a course on computer design and architecture. - Jesse Eickholt (J. Comput. Sci. Coll., 2015)

Incorporating economy hardware into a computer design and architecture course. - Jason Litchfield; Steven Ledsworth; Jesse Eickholt (J. Comput. Sci. Coll., 2015)

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. - Matt Spencer; Jesse Eickholt; Jianlin Cheng (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

A bioinformatics approach for exploring text-processing: calculating protein weights nifty assignment. - Jesse Eickholt; Edward J. Mirielli (J. Comput. Sci. Coll., 2016)

A distributed pipeline for DIDSON data processing. - Liling Li; Tyler Danner; Jesse Eickholt; Erin McCann; Kevin Pangle; Nicholas Johnson (IEEE BigData, 2017)

Supporting active learning through commodity and open source solutions. - Jesse Eickholt; John Roush; Patrick Seeling; Thiruvarangan Vedantham; Matthew R. Johnson 001 (FIE, 2017)

Levels of active learning in programming skill acquisition: From lecture to active learning rooms. - Patrick Seeling; Jesse Eickholt (FIE, 2017)

Teaching Big Data and Cloud Computing with a Physical Cluster. - Jesse Eickholt; Sharad Shrestha (SIGCSE, 2017)

Creating Economy Active Learning Classrooms for IT Students. - Quentrese Cole; Matthew R. Johnson 001; Jesse Eickholt (SIGITE, 2017)

Supporting active learning in computer science through technology and community. - Jesse Eickholt; Patrick Seeling; Matthew R. Johnson 001 (J. Comput. Sci. Coll., 2017)

Barriers to Active Learning for Computer Science Faculty. - Jesse Eickholt (CoRR, 2018)

Confronting barriers to active learning in computer science through technology, community and culture: tutorial presentation. - Jesse Eickholt; Patrick Seeling; Lisa Gandy; Quentrese Cole; Matthew R. Johnson 001 (J. Comput. Sci. Coll., 2018)

Advancing Adoption of Active Learning Pedagogy via New Avenues of Research and Training. - Jesse Eickholt; Lisa Gandy; Patrick Seeling; Matthew R. Johnson 001 (FIE, 2019)

Low-Cost Active Learning Benefits for Introductory Computer Science Courses. - Patrick Seeling; Jesse Eickholt; Quentrese Hinton; Matthew R. Johnson 001 (FIE, 2019)

Creating a culture and environment for active learning success. - Jesse Eickholt; Patrick Seeling; Lisa Gandy; Quentrese Cole; Matthew R. Johnson 001 (J. Comput. Sci. Coll., 2019)

Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics. - Jesse Eickholt (FIE, 2020)

Pedagogy and Classroom: How Can I Do This in That Space or Does it Even Matter? - Jesse Eickholt; Patrick Seeling (SIGCSE, 2020)

TopQA: a topological representation for single-model protein quality assessment with machine learning. - John Smith; Matthew Conover; Natalie Stephenson; Jesse Eickholt; Dong Si; Miao Sun; Renzhi Cao (Int. J. Comput. Biol. Drug Des., 2020)

LAGradebook: a tool for course-level comparative learning analytics. - Chistopher Phillips; Jesse Eickholt (J. Comput. Sci. Coll., 2020)

Practical Active Learning Stations to Transform Existing Learning Environments Into Flexible, Active Learning Classrooms. - Jesse Eickholt; Matthew R. Johnson 001; Patrick Seeling (IEEE Trans. Educ., 2021)

Student-facing Learning Analytics Dashboard: Profiles of Student Use. - Jesse Eickholt; Jennifer L. Weible; Stephanie D. Teasley (FIE, 2022)

An Ornithologist's Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio. - Ming Liu; Qiyu Sun; Dustin E. Brewer; Thomas M. Gehring; Jesse Eickholt (Remote. Sens., 2022)