Articles

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Kartchner, D., Nakajima An, D., Ren, W., Zhang, C., & Mitchell, C. S. (2022). Rule-Enhanced Active Learning for Semi-Automated Weak Supervision. AI, 3(1), 211-228.

Kirkpatrick, A., Onyeze, C., Kartchner, D., Allegri, S., Nakajima An, D., McCoy, K., … & Mitchell, C. S. (2022). Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0. Big Data and Cognitive Computing, 6(1), 27.

Tandon, R., Seyfried, N. T., & Mitchell, C. S. (2021). AI‐based selection of superior proteomic biomarkers for classifying Alzheimer’s disease. Alzheimer’s & Dementia, 17, e058741.

Prakash, J., Wang, V., Quinn III, R. E., & Mitchell, C. S. (2021). Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations. Brain Sciences, 11(8), 977.

McCoy, K., Gudapati, S., He, L., Horlander, E., Kartchner, D., Kulkarni, S., … & Mitchell, C. S. (2021). Biomedical text link prediction for drug discovery: A case study with COVID-19. Pharmaceutics, 13(6), 794.

Watson, Y., Nelson, B., Kluesner, J. H., Tanzy, C., Ramesh, S., Patel, Z., … & Mitchell, C. S. (2021). Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer’s Disease Mice. Journal of Alzheimer’s Disease, 83(1), 435-450.

Mohanavelu, P., Mutnick, M., Mehra, N., White, B., Kudrimoti, S., Hernandez Kluesner, K., Chen, X., Nguyen, T., Horlander, E., Thenot, H., Kota, V., Mitchell, C.S. (2021). Meta-analysis of Gastrointestinal Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia. Cancers 13:1643. DOI: https://doi.org/10.3390/cancers13071643.

Kirkpatrick, A., Patton, K., Tetali, P., Mitchell, C.S. (2020). Markov Chain-Based Sampling for Exploring RNA Secondary Structure under the Nearest Neighbor Thermodynamic Model and Extended Applications. Math. Comput. Appl. 25(4), 67. DOI: https://doi.org/10.3390/mca25040067.

Bond, L.; Bowen, G.; Mertens, B.; Denson, K.; Jordan, K.; Vidakovic, B.; Mitchell, C.S. (2020). Associations of Patient Mood, Modulators of Quality of Life, and Pharmaceuticals with Amyotrophic Lateral Sclerosis Survival Duration. Behavioral Sciences, 10(1), 33, doi: 10.3390/bs10010033

Bond, L., Ganguly P., Khamankar, N., Mallet, N., Bowen, G., Green, B., Mitchell C.S. (2019). A comprehensive examination of percutaneous endoscopic gastrostomy and its association with Amyotrophic Lateral Sclerosis patient outcomes. Brain Sci. 9:223. doi:10.3390/brainsci909022.

Sedler, A., Mitchell, C.S. (2019). SemNet: Using local features to navigate the biomedical concept graph. Front Bionengr & Biotech. 7:156. doi: 10.3389/fbioe.2019.00156

Khamankar, N., et al. (2018). “Associative Increases in Amyotrophic Lateral Sclerosis Survival Duration With Non-invasive Ventilation Initiation and Usage Protocols.” Frontiers in Neurology 9(578).

Pfohl, S. R., et al. (2018). “Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction.” Frontiers in Neuroinformatics 12(36).

Bond, L., et al. (2018). “A Metadata Analysis of Oxidative Stress Etiology in Preclinical Amyotrophic Lateral Sclerosis: Benefits of Antioxidant Therapy.” Frontiers in Neuroscience 12(10).

Huber CM, Yee C, May T, Dhanala A, Mitchell CS. Cognitive Decline in Preclinical Alzheimer’s Disease: Amyloid-Beta versus Tauopathy. Journal of Alzheimer’s Disease. 2017;61(1):265-281. doi:10.3233/JAD-170490.

Hollinger, S. K., Okosun, I.S., Mitchell, C.S. (2016). “Antecedent Disease and Amyotrophic Lateral Sclerosis: What Is Protecting Whom?” Front Neurol 7: 47.

Jeyachandran, A., Mertens, B., McKissick, E.A., Mitchell C.S. (2015). Type I Vs. Type II Cytokine Levels as a Function of SOD1 G93A Mouse Amyotrophic Lateral Sclerosis Disease Progression. Front. Cell. Neurosci. 9:462. doi: 10.3389/fncel.2015.00462

Mitchell, C.S., Cates, A., Kim, R.B., Hollinger, S.K. (2015). Undergraduate Biocuration: Developing Tomorrow’s Researchers While Mining Today’s Data. The Journal of Undergraduate Neuroscience Education (JUNE), 14(1):A56-A65.

Coan, G., & Mitchell, C. S. (2015). An Assessment of Possible Neuropathology and Clinical Relationships in 46 Sporadic Amyotrophic Lateral Sclerosis Patient Autopsies. Neurodegenerative Diseases. PMID: 26183171

Mitchell, C. S., Hollinger, S. K., Goswami, S. D., Polak, M. A., Lee, R. H., & Glass, J. D. (2015). Antecedent Disease Is Less Prevalent in Amyotrophic Lateral Sclerosis. Neurodegenerative Diseases, 15(2), 109-113. PMID: 25720304

Lee, R. H., & Mitchell, C. S. (2015). Axonal transport cargo motor count versus average transport velocity: is fast versus slow transport really single versus multiple motor transport? J Theor Biol, 370, 39-44. PMID: 25615423

Pfohl, S. R., Halicek, M. T., & Mitchell, C. S. (2015). Characterization of the Contribution of Genetic Background and Gender to Disease Progression in the SOD1 G93A Mouse Model of Amyotrophic Lateral Sclerosis: A Meta-Analysis. Journal of Neuromuscular Diseases, 2(2), 137-150.

Kim, R. B., Irvin, C. W., Tilva, K. R., & Mitchell, C. S. (2015). State of the field: An informatics-based systematic review of the SOD1-G93A amyotrophic lateral sclerosis transgenic mouse model. Amyotroph Lateral Scler Frontotemporal Degener, 1-14. PMID: 25998063

Irvin, C. W., Kim, R. B., & Mitchell, C. S. (2015). Seeking homeostasis: temporal trends in respiration, oxidation, and calcium in SOD1 G93A Amyotrophic Lateral Sclerosis mice. Front Cell Neurosci, 9, 248. PMID: 26190973

Foley, A. M., Ammar, Z. M., Lee, R. H., & Mitchell, C. S. (2015). Systematic review of the relationship between amyloid-beta levels and measures of transgenic mouse cognitive deficit in Alzheimer’s disease. J Alzheimers Dis, 44(3), 787-795. PMID: 25362040

Mitchell, C. S., & Lee, R. H. (2012). Cargo distributions differentiate pathological axonal transport impairments. J Theor Biol, 300, 277-291. PMID: 22285784

Mitchell, C.S. and Lee, R.H. (2012). Dynamic Meta-Analysis as a Therapeutic Prediction Tool for Amyotrophic Lateral Sclerosis. Amyotrophic Lateral Sclerosis. M. H. Maurer. Intech ISBN 979-953-307-199-1

Lee, R. H., & Mitchell, C. S. (2012). Revisiting the role of spike afterhyperpolarization and spike threshold in motoneuron current-frequency gain. J Neurophysiol, 107(11), 3071-3077. PMID: 22422996

Mitchell, C. S., & Lee, R. H. (2011). Synaptic glutamate spillover increases NMDA receptor reliability at the cerebellar glomerulus. J Theor Biol, 289, 217-224. PMID: 21884708

Mitchell, C. S., & Lee, R. H. (2011). The dynamics of somatic input processing in spinal motoneurons in vivo. J Neurophysiol, 105(3), 1170-1178. PMID: 21191091

Mitchell, C. S., & Lee, R. H. (2009). A quantitative examination of the role of cargo-exerted forces in axonal transport. J Theor Biol, 257(3), 430-437. PMID: 19150364

Mitchell, C. S., & Lee, R. H. (2008). Pathology dynamics predict spinal cord injury therapeutic success. J Neurotrauma, 25(12), 1483-1497. PMID: 19125684

Mitchell, C. S., & Lee, R. H. (2007). Output-based comparison of alternative kinetic schemes for the NMDA receptor within a glutamate spillover model. Journal of Neural Engineering, 4(4), 380-389. PMID: 18057505

Mitchell, C. S., Feng, S. S., & Lee, R. H. (2007). An analysis of glutamate spillover on the N-methyl-D-aspartate receptors at the cerebellar glomerulus. Journal of Neural Engineering, 4(3), 276-282. PMID: 17873430

Conference Abstracts