Journal Articles

61. L. Souza, J. Silva, M. Mendonça, J. Nathan, A. Duarte, P. Sarder, W. L. C. dos-Santos, and L. Oliveira, “The problem of segmenting global glomerulosclerosis in gigapixel histopathological images: The borderless glomeruli,” BMC Nephrology, vol. 26, pp. 544:1–13, Sept. 2025.

60. S. P. Border^†, R. M. Ferreira, N. Lucarelli^†, S. K. C. Kumar, A. S. Paul^Ω, D. Manthey, L. Barisoni, Y. A. L. Strekalova, J. Ray, Y. Cheng, A. Z. Rosenberg, J. E. Tomaszewski, S. Mimar^Ω, J. B. Hodgin, J. W. Hickey, B. Wei, F. Ginty, A. Karunamurthy, J. Wang, M. Raina, G. S. Pryhuber, J. Purkerson, T. M. El-Achkar, S. Jain, M. T. Eadon, and P. Sarder, “FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology,” Nature Communications, vol. 16, pp. 8388:1–15, Sept. 2025.

59. R. Galvin-Liu, L. Atchinson, J. Ray, S. P. Border^†, M. Khan, F. Afsari^§, P. Sarder, and Y. A. L. Strekalova, “An innovative approach to facilitating cross-disciplinary collaboration among STEM and public health students in biomedical AI and clinical translational research,” Biochemistry and Molecular Biology Education, pp. 70016:1–11, Sept. 2025.

58. A. Scheideman, M. Shao, H. Zelada, J. Cuadros, J. Foreman, P. Sarder, C. Ho, N. Ejskjaer, J. Fleischer, S. L. Cichosz, D. G. Armstrong, N. Mathioudakis, T. Wang, Y. C. Tham, and D. C. Klonoff, “Machine learning to diagnose complications of diabetes,” Journal of Diabetes Science and Technology, pp. 19322968251365245:1–21, Sept. 2025.

57. R. Galvin-Liu, N. Sherwin, S. Kavak, V. Liwang, S. P. Border^†, M. Khan, S. Jain, P. Sarder, and Y. A. L. Strekalova, “Shaping learning objectives for biomedical artificial intelligence: Student-centered insights into novel cell visualization technology,” Journal of Clinical and Translational Science, vol. 9, no. 1, e208:1–22, Aug. 2025.

56. M. Asghari, A. R. Sabo, D. Barwinska, R. M. Ferreira, M. J. Ferkowicz, W. S. Bowen, Y. Cheng, D. L. Gisch, C. Gulbronson, C. L. Phillips, K. J. Kelly, T. A. Sutton, J. C. Williams, M. Vazquez, J. O’Toole, P. Palevsky, S. E. Rosas, S. S. Waikar, K. Kiryluk, C. Parikh, J. Hodgin, P. Sarder, I. H. D. Boer, J. Himmelfarb, M. Kretzler, S. Jain, M. T. Eadon, S. Winfree, T. M. El-Achkar, and P. C. Dagher, “Integration of spatial protein imaging and transcriptomics in the human kidney tracks the regenerative potential of proximal tubules,” Science Advances, vol. 11, pp. eadv8918:1–18, Aug. 2025.

55. D. Yun, S. Bae, Y. Gao, L.Lopez, D. Han, C. D. Nicora, T. Y. Kim, K. C. Moon. D. K. Kim, T. L. Fillmore, Y. S. Kim, A. Z. Rosenberg, W. Wang, P. Sarder, W. J. Qian, M. Afkarian, and S. S. Han, “Complement proteins identify rapidly progressive diabetic kidney disease,” Kidney International Reports, vol. 10, pp. 7:2296–2310, May 2025.

54. O. Dasa, E. Handberg, D. Dey, P. Sarder, M. C. Lo, B. K. Tamarappoo, S. M. Smith, L. J. Shaw, C. N. B. Merz, and C. J. Pepine, “QUIET WARRIOR – Rationale and design: An ancillary study to the Women’s IschemiA TRial to reduce events in nonobstructive CAD (WARRIOR),” American Heart Journal Plus: Cardiology Research and Practice, vol. 51, pp. 100508:1–8, Mar. 2025.

53. Y. A. L. Strekalova, R. Galvin-Liu, S. P. Border^†, S. M. Midence, M. Khan, M. Vanzanten, J. E. Tomaszewski, S. Jain, and P. Sarder, “Summer research internship curriculum to promote self-efficacy, researcher identity, and peer-to-peer learning: Retrospective cohort study,” JMIR Formative Research, vol. 9, pp. e54167:1–10, Mar. 2025.

52. Y. A. L. Strekalova, S. Jain, and P. Sarder, “Developing evaluation metrics to measure the U.S. National Institutes of Health plans to enhance diverse perspectives: A protocol for a consensus-based study,” BMJ Open, vol. 15, pp. e087739:1–7, Feb. 2025.

51. T. Yoshida, K. Z. Latt, A. Z. Rosenberg, B. A. Santo^†, K. Myakala, Y. Ishimoto, Y. Zhao, S. Shrivastav, B. A. Jones, X. Yang, X. X. Wang, V. M. Tutino, P. Sarder, M. Levi, K. Okamoto, C. A. Winkler, and J. B. Kopp, “PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy,” eLife., vol. 12, pp. RP91260:1–21, Aug. 2024.

50. T. Yoshida, K. Myakala, B. A. Jones, X. X. Wang, S. Shrivastav, B. A. Santo^†, T. A. Patel, Y. Zhao, V. M. Tutino, P. Sarder, A. Z. Rosenberg, C. A. Winkler, M. Levi, and J. B. Kopp, “NAD deficiency contributes to progressive kidney disease in HIV nephropathy mice,” American Journal of Physiology – Renal Physiology, vol. 327, pp. 3:F450–F462, Aug. 2024.

49. T. Yoshida, K. Z. Latt, B. A. Santo^†, S. Shrivastav, Y. Zhao, P. Fenaroli, J. Y. Chung, S. M. Hewitt, V. M. Tutino, P. Sarder, A. Z. Rosenberg, C. A. Winkler, and J. B. Kopp, “Single-cell transcriptional signatures of glomerular disease in transgenic mice with APOL1 variants,” Journal of the American Society of Nephrology, vol. 35, pp. 8:1058–75, Aug. 2024.

48. S. P. Border^†, J. E. Tomaszewski, T. Yoshida, J. B. Kopp, J. B. Hodgin, W. L. Clapp, A. Z. Rosenberg, J. P. Buyon, and P. Sarder, “Investigating quantitative histological characteristics in renal pathology using HistoLens,” Scientific Reports – Nature, vol. 14, pp. 17528:1–13, Jul. 2024.

47. K. Siew, ………….., P. Sarder, …….S. B. Walsh, “Cosmic kidney disease: An integrated pan-omic, physiological, and morphological study into spaceflight-induced renal dysfunction,” Nat Commun, vol. 15, pp. 4923:1–20, Jun 2024.

46. B. Shepard, J. Chau, R. Kurtz, A. Z. Rosenberg, P. Sarder, S. P. Border^†, B. Ginley^§, O. Rodriguez, C. Albanese, G. Knoer, A. Greene, A. Maria A. de Souza, S. Ranjit, M. Levi, and C. Ecelbarger, “Nascent shifts in renal cellular metabolism, structure, and function due to chronic empagliflozin in pre-diabetic mice,” American Physiological Society, vol. 326, pp. 4:1272–1290, Apr. 2024.

45. C. Roufosse, M. Naesens, M. Haas, C. Lefaucheur, R. B. Mannon, M. Afrouzian, N. Alachkar, O. Aubert, S. M. Bagnasco, I. Batal, C. O. C. Bellamy, V. Broecker, K. Budde, M. Clahsen-Van Groningen, S. M. Coley, L. D. Cornell, D. Dadhania, A. J. Demetris, G. Einecke, A. B. Farris, A. B. Fogo, J. Friedewald, I. W. Gibson, C. Horsfield, E. Huang, S. A. Husain, A. M. Jackson, J. Kers, Z. Kikic, A. Klein, N. Kozakowski, H. Liapis, M. Mangiola, R. A. Montgomery, B. Nankinvell, D. A. H. Neil, P. Nickerson, M. Rabant, P. Randhawa, L. V. Riella, I. Rosales, V. Royal, R. Sapir-Pichhadze, P. Sarder, M. Sarwal, C. Schinstock, M. Stegall, K. Solez, J. van der Laak, C. Wiebe, R. B. Colvin, A. Loupy, and M. Mengel, “The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts,” American Journal of Transplantation, vol. 24, pp. 3:350–361, Mar. 2024.

44. N. Lucarelli^†, B. Ginley^§, J. Zee, S. Mimar^Ω, A. S. Paul^Ω, S. Jain, S. S. Han, L. Rodrigeus, T. Ozrazgat-Baslanti, M. L. Wong, G. Nadkarni, W. L. Clapp, K. Y. Jen, and P. Sarder, “Correlating deep learning-based automated reference kidney histomorphometry with patient demographics and creatinine,” Kidney 360, vol. 4, pp. 12:1726–1737, Dec. 2023. [Selected to be on the cover of the respective issue.]

43. A. B. Farris, M. P. Alexander, U. G. J. Balis, L. Barisoni, P. Boor, R. D. Bülow, L. D. Cornell, A. J. Demtris, E. Farkash, M. Hermsen, J. Hogan, R. Kain, J. Kers, J. Kong, R. M. Levenson, A. Loupy, M. Naesens, P. Sarder, J. E. Tomaszewski, J. van der Laak, D. van Midden, Y. Yagi, and K. Solez, “Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments,” Transplant International, vol. 36, pp. 11783:1–9, Oct. 2023.

42. K. Myakala, X. X. Wang, N. V. Shults, E. Krawczyk, B. A. Jones, X. Yang, A. Z. Rosenberg, B. Ginley^†, P. Sarder, L. Brodsky, Y. Jang, C. H. Na, Y. Qi, X. Zhang, U. Guha, C. Wu, S. Bansal, J. Ma, A. Cheema, C. Albanese, M. D. Hirschey, T. Yoshida, J. B. Kopp, J. Panov, and M. Levi, “NAD metabolism modulates inflammation and mitochondria function in diabetic kidney disease,” Journal of Biological Chemistry, vol. 299, pp. 8:1–28, Aug. 2023.

41. G. Vasquez-Rios, W. Oh, S. Lee, P. Bhatraju, S. G. Mansour, D. G. Moledina, F. F. Gulamali, E. Siew, A. X. Garg, P. Sarder, V. M. Chinchilli, J. S. Kaufman, C. Y. Hsu, K. D. Liu, P. L. Kimmel, A. S. Go, M. M. Wurfel, J. Himmelfarb, C. R. Parikh, S. G. Coca, and G. N. Nadkarni, “Joint modeling of clinical and biomarker data in acute kidney injury defines unique sub-phenotypes with differing outcomes,” Clinical Journal of the American Society of Nephrology, vol. 18, pp. 6:716–726, Jun. 2023.

40. R. Calumby, A. Duarte, M. F. Angelo, E. Santos, P. Sarder, W. L. C. Dos-Santos, and L. Oliveira, “Towards real-world computational nephropathology,” Clinical Journal of the American Society of Nephrology, vol. 18, pp. 6:809–812, Jun. 2023.

39. S. Border^†, N. Lucarelli^†, M. T. Eadon, T. M. El-Achkar, S. Jain, and P. Sarder, “Computational pathology fusing spatial technologies,” Clinical Journal of the American Society of Nephrology, vol. 18, pp. 5:675–677, May. 2023.

38. X. Zheng, L. Higdon, A. Gaudet, M. Shah, A. Balistieri, C. Li, P. Nadai, L. Palaniappan, X. Yang, B. A. Santo^†, B. Ginley^†, X. X. Wang, K. Myakala, P. Nallagatla, M. Levi, P. Sarder, A. Rosenberg, J. S. Maltzman, N. de Freitas Caires, and V. Bhalla, “Endothelial cell-specific molecule-1 inhibits albuminuria in diabetic mice,” Kidney360, vol. 3, no. 12, pp. 2059-2076, Jul. 2022. [Editorial on this article: J. H. Holthoff, K. Chandrashekar, and L. A. Juncos, “The role of Esm-1 in diabetic kidney disease: More than just a biomarker,” Kidney360, vol. 3, pp. 12:1998–2000, Dec. 2022.]

37. M. Andrews, T. Yoshida, C. M. Henderson, H. Pflaum, A. McGregor, J. A. Lieberman, I. H. de Boer, T. Vaisar, J. Himmelfarb, B. Kestenbaum, J. Y. Chung, S. M. Hewitt, B. A. Santo^†, B. Ginley^†, P. Sarder, A.Z. Rosenberg, T. Murakami, J. B. Kopp, Z. Kuklenyik, and A. N. Hoofnagle, “Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury,” Plos One, vol. 17, no. 10, pp. e0276649:1–20, Oct. 2022.

36. B. Lutnick^†, D. Manthey, J. U. Becker, B. Ginley^†, K. Moos, J. E. Zuckerman, L. Rodrigues, A. J. Gallan, L. Barisoni, C. E. Alpers, X. X. Wang, K. Myakala, B. A. Jones, M. Levi, J. B. Kopp, T. Yoshida, S. S. Han, S. Jain, A. Z. Rosenberg, K. Y. Jen, and P. Sarder, for the Kidney Precision Medicine Project, “A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology,” Communications Medicine (London), vol. 2, pp. 105:1–15, Aug. 2022.

35. B. A. Santo^†, D. Govind^†, P. Daneshpajouhnejad, X. Yang, X. X. Wang, K. Myakala, B. A. Jones, M. Levi, J. B. Kopp, L. J. Niedernhofer, D. Manthey, K. C. Moon, S. S. Han, J. Zee, A. Z. Rosenberg, and P. Sarder, “PodoCount: A robust, fully automated whole-slide podocyte quantification tool,” Kidney International Reports, vol. 7, pp. 6:1377–92, Jun. 2022.

34. B. Lutnick^†, D. Manthey, J. U. Becker, J. E. Zuckerman, L. Rodrigues, K. Y. Jen, and P. Sarder, “A tool for federated training of segmentation models on whole slide images,” Journal of Pathology Informatics – Elsevier, vol. 13, pp. 100101:1–7, May 2022.

33. U. Muhammad, Md. Z. Hoque, M. Oussalah, A. Keskinarkaus, T. Seppänena, and P. Sarder, “SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images,” Knowledge-Based Systems – Elsevier, vol. 241, pp. 108207:1–12, Apr. 2022.

32. A. Sabharwal^∗, N. Kavthekar^†,∗, J. Miecznikowski, M. Glogauer, A. Maddi, and P. Sarder, “Integrating image analysis and dental radiography for periodontal and peri-implant diagnosis,” Frontiers in Dental Medicine, vol. 3, pp. 840963:1–10, Mar. 2022.

31. B. Lutnick^†, L. K. Murali^†, B. Ginley^†, A. Z. Rosenberg, and P. Sarder, “Histo-fetch – On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training,” Journal of Pathology Informatics – Elsevier, vol. 13, pp. 7:1–5, Jan. 2022.

30. S. P. Border^†, and P. Sarder, “From what to why, the growing need for a focus shift towards explainability of AI in digital pathology,” Frontiers in Physiology, vol. 12, pp. 821217:1–7, Jan. 2022.

29. D. Govind^†, J. U. Becker, J. Miecznikowski, A. Z. Rosenberg, J. Dang, P. L. Tharaux, R. Yacoub, F. Thaiss, P. F. Hoyer, D. Manthey, B. Lutnick, A. M. Worral, I. Mohammad, V. Walavalkar, J. E. Tomaszewski, K. Y. Jen, and P. Sarder, “PodoSighter: A cloud-based tool for label-free podocyte detection in kidney whole-slide images,” Journal of the American Society of Nephrology, vol. 32, pp. 11:2795–2813, Nov. 2021. [Selected to be on the cover of the respective issue.]

28. B. Ginley^†,*, K. Y. Jen^*, S. S. Han, L. Rodrigues, S. Jain, A. Fogo, J. Zuckerman, V. Walavalkar, J. C. Miecznikowski, Y. Wen, F. Yen, D. Yun, K. C. Moon, A. Rosenberg, C. Parikh, and P. Sarder, “Automated computational detection of interstitial fibrosis, tubular atrophy, and glomerulosclerosis,” Journal of the American Society of Nephrology, vol. 32, no. 4, pp. 837–50, Apr. 2021. [Featured article of the respective issue. Editorial on this article: J. B. Hodgin and L.H. Mariani, “Automated quantification of chronic changes in the kidney biopsy: Another step in the right direction,” Journal of the American Society of Nephrology, vol. 32, pp. 4:767–68, Apr. 2021.]

27. L. Rodrigues, P. Sarder, K. Y. Jen, B. Ginley^†, J. Pratas, V. Sousa, A. Figueiredo, and R. Alves, “The rediscovery of nephropathology with artificial intelligence,” Portuguese Journal of Nephrology and Hypertension, vol. 34, pp. 4:195–97, Jan. 2021.

26. D. Govind^†,*, K. Y. Jen^*, K. Matsukuma, G. Gao, K. A. Olson, D. Gui, G. Wilding, S. P. Border^†, and P. Sarder, “Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning,” Scientific Reports – Nature, vol. 10, pp. 11064:1–12, Jul. 2020.

25. B. A. Santo^†, A. Rosenberg, and P. Sarder, “Artificial intelligence driven next-generation renal histomorphometry,” Current Opinion in Nephrology and Hypertension, vol. 29, pp. 3:265–72, May 2020.

24. B. Ginley^†, B. Lutnick^†, K. Y. Jen, A. Fogo, S. Jain, A. Rosenberg, V. Walavalkar, G. Wilding, J. E. Tomaszewski, R. Yacoub, G. M. Rossi, and P. Sarder, “Computational segmentation and classification of diabetic glomerulosclerosis,” Journal of the American Society of Nephrology, vol. 30, no. 10, pp. 1953–67, Oct. 2019. [Featured article of the respective issue. The article was selected for the Best of American Society of Nephrology (ASN) Journals: Clinical Journal of ASN (CJASN) & JASN in 2019. Editorial on this article: K. V. Lemley, “Machine learning comes to nephrology,” Journal of the American Society of Nephrology, vol. 30, pp. 10:1780–81, Oct. 2019.]

23. B. Lutnick^†, B. Ginley^†, D. Govind^†, S. D. McGarry, P. S. LaViolette, R. Yacoub, S. Jain, J. E. Tomaszewski, K. Y. Jen, and P. Sarder, “An integrated iterative annotation technique for easing neural network training in medical image analysis,” Nature Machine Intelligence, vol. 1, pp. 2:112–19, Feb. 2019.

22. D. Maji, J. Lu, P. Sarder, A. H. Schmieder, G. Cui, X. Yang, D. Pan, M. D. Lew, S. Achilefu, and G. M. Lanza, “Cellular trafficking of Sn-2 phosphatidylcholine prodrugs studied with fluorescence lifetime imaging and super-resolution microscopy,” Prec. Nanomed., vol. 1, pp. 2:127–45, Jul. 2018. [Selected as Feature Article for the 2018(2) issue.]

21. D. Govind^†, B. Lutnick^†, J. E. Tomaszewski, and P. Sarder, “Automated erythrocyte detection and classification from whole slide images,” Journal of Medical Imaging – SPIE, vol. 5, no. 2, pp. 027501:1–11, Apr. 2018.

20. O. Simon^†, R. Yacoub, S. Jain, J. E. Tomaszewski, and P. Sarder, “Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images,” Scientific Reports – Nature, vol. 8, pp. 2032:1–11, Feb. 2018.

19. B. Ginley^†, T. Emmons, B. Lutnick^†, C. Urban, B. H. Segal, and P. Sarder, “Computational detection and quantification of human and mouse neutrophil extracellular traps in flow cytometry and confocal microscopy,” Scientific Reports – Nature, vol. 7, pp. 17755:1–11, Dec. 2017.

18. E. K. Stachowiak, C. A. Benson, S. T. Narla, A. Dimitri, L. Chuye, S. Dhiman, K. Harikrishnan, S. Elahi, D. Freedman, K. Brennard, P. Sarder, and M. K. Stachowiak, “Cerebral organoids reveal early cortical maldevelopment in schizophrenia — computational anatomy and genomics, role of FGFR1,” Translational Psychiatry – Nature, vol. 7, pp. 6:1–24, Nov. 2017.

17. S. Narla, Y-W. Lee, C. A. Benson, P. Sarder, K. J. Brennand, E. K. Stachowiak, and M. K. Stachowiak, “Common developmental genome deprogramming in schizophrenia — Role of Integrative Nuclear FGFR1 Signaling (INFS),” Schizophrenia Research, vol. 185, pp. 17–32, Jul. 2017.

16. B. Ginley^†, J. E. Tomaszewski, R. Yacoub, F. Chen, and P. Sarder, “Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology,” Journal of Medical Imaging – SPIE, vol. 4, no. 2, pp. 021102:1–13, Feb. 2017.

15. E. Ringhausen, T. Wang, J. Pitts, P. Sarder, and W. Akers, “Evaluation of dynamic optical projection of acquired luminescence for sentinel lymph node biopsy in large animals,” Technology in Cancer Research & Treatment, vol. 15, pp. 6:787–795, Dec. 2016.

14. R. Gilson, R. Tang, A. Som, C. Klajer, P. Sarder, G. Sudlow, W. Akers, and S. Achilefu, “Protonation and trapping of a small pH-sensitive near-infrared fluorescent molecule in acidic tumor environment delineates diverse tumors in vivo,” Molecular Pharmaceutics, vol. 12, pp. 12:4237–4246, Oct. 2015.

13. P. Sarder, D. Maji, and S. Achilefu, “Molecular probes for fluorescence lifetime imaging,” Bioconjugate Chemistry, vol. 26, pp. 6:963–974, May 2015.

12. X. Xu, Z. Li, P. Sarder, N. Kotagiri, and A. Nehorai, “Simultaneous detection of multiple biological targets using optimized microfluidic microsphere-trap arrays,” Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 13, no. 1, pp. 13017:1–10, Jan. 2014.

11. D. Hu^*, P. Sarder^*, P. Ronhovde, S. Orthaus, S. Achilefu, and Z. Nussinov, “Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection–a first study,” Journal of Microscopy, vol. 253, pp. 1:54–64, Jan. 2014.

10. P. Sarder, K. Gullicksrud, S. Mondal, G. Sudlow, S. Achilefu, and W. Akers, “Dynamic optical projection of acquired luminescence for aiding oncologic surgery,” Journal of Biomedical Optics, vol. 253, no. 12, pp. 12501:1–3, Dec. 2013.

9. P. Sarder, S. Yazdanfar, W. Akers, R. Tang, G. Sudlow, C. Egbulefu, and S. Achilefu, “All–near-infrared multiphoton microscopy interrogates intact tissues at deeper imaging depths than conventional single and two photon near-infrared excitation microscopes,” Journal of Biomedical Optics, vol. 18, no. 10, pp. 106012:1–11, Oct. 2013.

8. X. Xu, P. Sarder, N. Kotagiri, S. Achilefu, and A. Nehorai, “Performance analysis and design of position-encoded microsphere arrays using the Ziv-Zakai bound,” IEEE Trans. on NanoBioscience, vol. 12, pp. 1:29–40, Mar. 2013.

7. X. Xu^*, P. Sarder^*, Z. Li^*, and A. Nehorai, “Optimization of microfluidic microsphere-trap arrays,” Biomicrofluidics, vol. 7, no. 1, pp. 014112:1–16, Feb. 2013.

6. R. Nothdurft*, P. Sarder^*, S. Bloch, J. Culver, and S. Achilefu, “Fluorescence lifetime imaging microscopy using near-infrared contrast agents,” Journal of Microscopy, vol. 247, pp. 2:202–207, Aug. 2012.

5. P. Sarder, and A. Nehorai, “Statistical design of position-encoded microsphere arrays,” IEEE Trans. on NanoBioscience, vol. 10, pp. 1:16–29, Mar. 2011.

4. P. Sarder, W. Schierding, J. P. Cobb, and A. Nehorai, “Estimating sparse gene regulatory networks using a Bayesian linear regression,” IEEE Trans. on NanoBioscience, vol. 9, pp. 2:121–131, June 2010.

3. P. Sarder, and A. Nehorai, “Estimating locations of quantum-dot–encoded microparticles from ultra-high density 3D microarrays,” IEEE Trans. on NanoBioscience, vol. 7, pp. 4:284–297, Dec. 2008.

2. P. Sarder, A. Nehorai, P. H. Davis, and S. Stanley, “Estimating gene signals from noisy microarray images,” IEEE Trans. on NanoBioscience, vol. 7, pp. 2:142–153, June 2008.

1. P. Sarder, and A. Nehorai, “Deconvolution methods for 3D fluorescence microscopy images: An overview,” IEEE Signal Processing Magazine, vol. 23, no. 3, pp. 32–45, May 2006. [“Sarder and Nehorai review perhaps the most heavily studied signal processing topic related to fluorescence microscopy: the deconvolution of images to remove blurring inherent in microscope image acquisition.” — J. Kovačević and R. F. Murphy (commented in their article, “Molecular and cellular bioimaging,” IEEE Signal Processing Magazine, vol. 23, no. 3, p. 19, May 2006).]