Krishna R. Kalari, Ph.D.
Affiliations: | 2006 | University of Iowa, Iowa City, IA |
Area:
Biomedical Engineering, Molecular Biology, Bioinformatics Biology, PathologyGoogle:
"Krishna Kalari"Parents
Sign in to add mentor| Todd E. Scheetz | grad student | 2006 | University of Iowa | |
| (Computational approach to identify deletions or duplications within a gene.) | ||||
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Publications
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| Baheti S, Tang X, O'Brien DR, et al. (2018) HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data. Bmc Bioinformatics. 19: 271 |
| Athreya AP, Gaglio AJ, Cairns J, et al. (2018) Machine Learning helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer. Ieee Transactions On Nanobioscience |
| Wieben ED, Aleff RA, Tang X, et al. (2018) Gene expression in the corneal endothelium of Fuchs endothelial corneal dystrophy patients with and without expansion of a trinucleotide repeat in TCF4. Plos One. 13: e0200005 |
| Bidadi B, Liu D, Kalari KR, et al. (2018) Pathway-Based Analysis of Genome-Wide Association Data Identified SNPs inas Biomarker for Chemotherapy- Induced Neutropenia in Breast Cancer Patients. Frontiers in Pharmacology. 9: 158 |
| Athreya AP, Kalari KR, Cairns J, et al. (2017) Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer. Oncotarget. 27199-27215 |
| Niu N, Liu T, Cairns J, et al. (2016) Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines. Human Molecular Genetics. 25: 4819-4834 |
| Niu N, Liu T, Cairns J, et al. (2016) Metformin Pharmacogenomics: A genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines. Human Molecular Genetics |
| Shameer K, Tripathi LP, Kalari KR, et al. (2015) Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment. Briefings in Bioinformatics |
| Perez EA, Thompson EA, Ballman KV, et al. (2015) Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the North Central Cancer Treatment Group n9831 Adjuvant Trastuzumab Trial. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 33: 701-8 |
| Tong Y, Niu N, Jenkins G, et al. (2014) Identification of genetic variants or genes that are associated with Homoharringtonine (HHT) response through a genome-wide association study in human lymphoblastoid cell lines (LCLs). Frontiers in Genetics. 5: 465 |