Donna Slonim -- Publications

Research areas include:

Molecular networks
Developmental genomics
Cancer classification and clinical pharmacogenomics
Transcriptional analysis
Genome mapping
Machine learning
Miscellaneous publications

Molecular Networks

GRN-VAE: A simplified and stabilized SEM model for gene regulatory network inference. Zhu H, Slonim D. bioRxiv: 2023.01.26.525733v1.

Open community challenge reveals molecular network modules with key roles in diseases. Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Lamparter D, Lin J, Hescott BJ, Hu X, Mercer J, Natoli T, Narayan R, Subramanian A, Stolovitzky G, Kutalik Z, Slonim DK, Saez-Rodriguez J, Cowen LJ, Bergmann S, Marbach D. Nature Methods, Sep;16(9):843-852, 2019.

Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease. Park J, Hescott B, Slonim DK. Scientific Reports, 9(1):5863, 2019.

A double-spectral approach to disease module identification (DREAM 11 subchallenge 3). Crawford J, Lin J, Hu X, Hescott B, Slonim D, Cowen L. RECOMB RSG/DREAM, Phoenix, AZ, 2016.

Connectedness of PPI network neighborhoods identifies regulatory hub proteins. Andrew D. Fox, Benjamin J. Hescott, Anselm Blumer, and Donna K. Slonim. Bioinformatics, Apr 15;27(8):1135-42, 2011.

Mining protein-protein interactions from GeneRIFs with OpenDMAP. Andrew D. Fox, William A. Baumgartner Jr., Helen Johnson, Lawrence E. Hunter and Donna K. Slonim. Lecture Notes in Computer Science, 6004:43-52, May, 2010.

Evaluating between-pathway models with expression data. Benjamin Hescott, Mark Leiserson, Lenore Cowen, Donna Slonim. Journal of Computational Biology, 17(3):477-87, March, 2010.

Toward the dynamic interactome: It's about time. Teresa M. Przytycka, Mona Singh, and Donna K. Slonim. Briefings in Bioinformatics, 11(1):15-29, Jan. 2010.

Dynamics of biological networks -- session introduction. Tanya Y. Berger-Wolf, Teresa M. Przytycka, Mona Singh, and Donna K. Slonim. Pacific Symposium on Biocomputing, 15:120-122, Jan. 2010.

High throughput interaction data reveals degree conservation of hub proteins. Andrew Fox, Daniel Taylor, and Donna K. Slonim. Pacific Symposium on Biocomputing, 14:391-402, 2009.

Developmental Genomics

Intergenerational effects of preconception opioids on glucose homeostasis and hepatic transcription in adult male rats. Toorie A.,Vassoler F., Qu F., Slonim DK, Schonoff C., Byrnes E. Sci. Rep., 12(1):1599, 2022.
HNRNP L is essential for myogenic differentiation and modulates myotonic dystrophy pathologies. Alexander MS, Hightower RM, Reid AL, Bennett AH, Iyer L, Slonim DK, Saha M, Kawahara G, Kunkel LM, Kopin AS, Gupta V, Kang PB, Draper I. Muscle & Nerve, 63(6):928-40, 2021.
A history of opioid exposure in females increases the risk of metabolic disorders in their future male offspring. Toorie A.,Vassoler F., Qu F., Schonoff C., Bradburn S., Murgatroyd C., Slonim D., Byrnes E. Addiction Biology, 26(1):e12856, 2021.

Amniotic fluid transcriptomics reflects novel disease mechanisms in fetuses with myelomeningocele. Tarui T, Kim A, Flake A, McClain L, Stratigis JD, Fried I, Newman R, Slonim DK, Bianchi DW. Am J Obstet Gynecol., Nov;217(5):587.e1-587.e10, 2017.

The fetal brain transcriptome and neonatal behavioral phenotype in the Ts1Cje mouse model of Down syndrome. Guedj F, Pennings JL, Ferres MA, Graham LC, Wick HC, Miczek KA, Slonim DK, Bianchi DW. Am J Med Genet Part A 167A(9):1993-2008, 2015.

The pathway not taken: understanding 'omics data in the perinatal context. Edlow AG, Hui L, Wick HC, Slonim DK, Bianchi DW. Am J Obst & Gynec. Jul;213(1):59.e1-59.e172, 2015.

Finding novel molecular connections between developmental processes and disease. Park J, Wick HC, Kee DE, Noto K, Maron JL, Slonim DK. PLoS Computational Biology, 10(5):e1003578, 2014.

Amniotic fluid RNA gene expression profiling provides insights into the phenotype of Turner syndrome. Massingham LJ, Johnson KL, Scholl TM, Slonim DK, Wick HC, Bianchi DW. Hum Genet. Sep;133(9):1075-82, 2014.

DFLAT: functional annotation for human development. Wick HC, Drabkin H, Ngu H, Sackman M, Fournier C, Haggett J, Blake JA, Bianchi DW, Slonim DK. BMC Bioinformatics. Feb 7;15:45, 2014.

Novel neurodevelopmental information revealed in amniotic fluid supernatant from fetuses with trisomies 18 and 21. Hui L, Slonim DK, Wick HC, Johnson KL, Bianchi DW. Human Genet., Nov;131(11):1751-9, 2012.

Comprehensive analysis of genes expressed by rare microchimeric fetal cells in maternal lung. Pritchard S, Wick HC, Slonim DK, Johnson KL, Bianchi DW. Biol. of Reproduction, Aug 23;87(2):42, 2012.

The amniotic fluid transcriptome: a source of novel information about human fetal development. Hui L, Slonim DK, Wick HC, Johnson KL, Bianchi DW. Obstetrics and Gynecology, Jan 119(1):111-8, 2012.

Transcriptomic analysis of cell-free fetal RNA suggests a specific molecular phenotype in trisomy 18. Koide K, Slonim DK, Johnson KL, Tantravahi U, Cowan JM, Bianchi DW. Hum Genet., 129(3):295-305, Mar. 2011.

Lack of nAChR Activity Depresses Cochlear Maturation and Up-Regulates GABA System Components: Temporal Profiling of Gene Expression in a9 Null Mice Sevin Turcan, Donna K. Slonim, and Doug E. Vetter. PLoS One, 5(2):e9058, Feb. 2010.

Functional genomic analysis of amniotic fluid cell-free mRNA suggests that oxidative stress is significant in Down syndrome fetuses. Donna K. Slonim*, Keiko Koide*, Kirby L. Johnson, Umadevi Tantravahi, Janet M. Cowan, Zina Jarrah, Diana W. Bianchi. Proceedings of the National Academy of Sciences USA, 106(23):9425-9, 2009.

Gene expression analysis in pregnant women and their infants identifies unique fetal biomarkers that circulate in maternal blood. Jill L. Maron, Kirby L. Johnson, Donna Slonim, Chao-Qiang Lai, Marco Ramoni, Gil Alterovitz, Zina Jarrah, Zinger Yang, and Diana W. Bianchi. Journal of Clinical Investigation, 117(10):3007-19, 2007.

Synthetic lethal analysis of C. elegans identifies conserved genetic interactions. L. Ryan Baugh, Joanne C. Wen, Andrew A. Hill, Donna K. Slonim, Eugene L. Brown, and Craig P. Hunter. Genome Biology, 6(4):R45, March, 2005.

The homeodomain protein PAL-1 specifies a lineage-specific regulatory network in the C. elegans embryo. L. Ryan Baugh, Andrew A. Hill, Julia M. Claggett, Kate Hill-Harfe, Joanne C. Wen, Donna K. Slonim, Eugene L. Brown, and Craig P. Hunter. Development 132(8):1843-54, March, 2005.

Composition and dynamics of the Caenorhabditis elegans early embryonic transcriptome . L. Ryan Baugh, Andrew A. Hill, Donna K. Slonim, Eugene L. Brown, and Craig P. Hunter. Development, 130(5):889-900, March, 2003.

Cancer Classification, Clinical Pharmacogenomics, Disease

FiT: fiber-based tensor completion for drug repurposing Bumin A, Ritz A, Slonim D, Kahveci T, Huang K. ACM-BCB 31:1-10, August, 2022. Winner, Best Student-Led Paper Award at ACM-BCB 2022.

Cell-specific imputation of drug connectivity mapping with incomplete data. Sapashnik D, Newman R, Pietras CM, Zhou D, Qu F, Devkota K, Kofman L, Boudreau S, Fried I, Slonim DK. PLoS ONE; 18(2):e0278289, 2023.

Towards a more molecular taxonomy of disease. Park J, Hescott BJ, Slonim DK J Biomed Semantics, Jul 27;8(1):25, 2017.

Risk factors associated with beta-amyloid(1-42) immunotherapy in preimmunization gene expression patterns of blood cells. Margot O'Toole, Derek B. Janszen, Donna K. Slonim, Padmalatha S. Reddy, Debra K. Ellis, Holly M. Legault, Andrew A. Hill, Maryann Z. Whitley, William M. Mounts, Krystina Zuberek, Frederick W. Immermann, Ronald S. Black, and Andrew J. Dorner. Archives of Neurology, 62:1531-6 October, 2005. Editorial about the article, from the same issue.

Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma. Michael E. Burczynski, Natalie C. Twine, Gary Dukart, Bonnie Marshall, Manuel Hidalgo, Walter Stadler, Theodore Logan, Janice Dutcher, Gary Hudes, William L. Trepicchio, Andrew Strahs, Fred Immermann, Donna K. Slonim, and Andrew J. Dorner. Clinical Cancer Research, 11(3):1181-1189, Feb, 2005.

Disease-associated expression profiles in PBMCs from patients with advanced renal cell carcinoma. Natalie C. Twine, Jennifer A. Stover, Bonnie Marshall, Gary Dukart, Manuel Hidalgo, Walter Stadler, Theodore Logan, Janice Dutcher, Gary Hudes, Andrew J. Dorner, Donna K. Slonim, William L. Trepicchio, and Michael E. Burczynski. Cancer Research, 63:6069-6075, September 15, 2003.

Chemosensitivity prediction by transcriptional profiling. Jane E. Staunton, Donna K. Slonim, Hilary A. Coller, Pablo Tamayo, Michael J. Angelo, Johnny Park, Uwe Scherf, Jae K. Lee, William O. Reinhold, John N. Weinstein, Eric S. Lander, Todd R. Golub. Proceedings of the National Academy of Sciences USA, 98(19):10787-10792, September, 2001.

Transcriptional profiling in cancer: the path to clinical pharmacogenomics. Donna K. Slonim. Pharmacogenomics, 2(2):123-136, May, 2001.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Todd R. Golub*, Donna K. Slonim*, Pablo Tamayo, Christine Huard, Michelle Gassenbeek, Jill Mesirov, Hilary Coller, Mignon Loh, James R. Downing, Mark A. Caligiuri, Clara D. Bloomfield, and Eric S. Lander. Science, 286(5439):531-537, October, 1999.

Transcriptomic Analysis Methods

aTEMPO: Pathway-Specific Temporal Anomalies for Precision Therapeutics. Pietras CM, Power L, and Slonim DK. Pac. Symp. Biocomp. 25:683-694, Jan. 2020.

TEMPO: Detecting pathway-specific temporal dysregulation of gene expression in disease. Pietras CM, Ocitti F, Slonim DK. Proceedings of the ACM conference on Bioinformatics and Computational Biology (ACM-BCB), pp. 79-88, Aug. 29-Sept.1, 2018. Updated significance assessment methods and results in bioRxiv.

Time is money: Designing cost-effective time series experiments, Donna K. Slonim. Cell Systems, 3(1):15-16, 2016.

CSAX: Characterizing Systematic Anomalies in eXpression Data, (Journal version) Keith Noto, Saeed Majidi, Andrea G. Edlow, Heather C. Wick, Diana W. Bianchi, and Donna K. Slonim. Journal of Computational Biology, May;22(5):402-13, 2015.

CSAX: Characterizing Systematic Anomalies in eXpression Data (conference version), Keith Noto, Carla Brodley, Saeed Majidi, Diana W. Bianchi, and Donna K. Slonim. RECOMB, pp. 222–236, 2014.

Mining functionally relevant gene sets for analyzing physiologically novel clinical expression data. Sevin Turcan, Doug E. Vetter, Jill L. Maron, Xintao Wei, and Donna K. Slonim. Pacific Symposium on Biocomputing, 16:50-61, Jan. 2011.

Getting started in gene expression microarray analysis. Donna K. Slonim and Itai Yanai. PLoS Computational Biology, 5(10):e1000543, Oct. 2009.

Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression. Yanai I, Baugh LR, Smith JJ, Roehrig C, Shen-Orr SS, Claggett JM, Hill AA, Slonim DK, and Hunter CP. Molecular Systems Biology, 4:163, 2008.

From patterns to pathways: gene expression data analysis comes of age. Donna K. Slonim. Nature Genetics, 32(S):502-508, December, 2002.

Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls. Andrew A. Hill, Eugene L. Brown, Maryann Z. Whitley, Greg Tucker-Kellogg, Craig P. Hunter, and Donna K. Slonim. Genome Biology, 2(12):R55, November, 2001.

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Atul J. Butte, Pablo Tamayo, Donna Slonim, Todd R. Golub, Isaac S. Kohane. Proceedings of the National Academy of Sciences USA, 97(22):12182-12186, October, 2000.

Class prediction and discovery using gene expression data. Donna K. Slonim, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub, and Eric S. Lander. Proceedings of the Fourth Annual Conference on Computational Molecular Biology (RECOMB) , 263-272, April, 2000.

Interpreting gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky, Eric S. Lander, and Todd R. Golub. Proceedings of the National Academy of Sciences USA, 96(6):2907-2912, March, 1999.

Genome Maps and Mapping Methods

A YAC-based physical map of the mouse genome. Chad Nusbaum*, Donna K. Slonim*, Katrina L. Harris, Bruce W. Birren, Robert G. Steen, Lincoln D. Stein, Joyce Miller, William F. Dietrich, Robert Nahf, Victoria Wang, Olga Merport, Andrew B. Castle, Zeeshan Hussain, Gail Farino, Delphine Gray, Mechele O. Anderson, Richard Devine, Lloyd T. Horton Jr., Dorothy F. Courtney, James Tam, Matthew Cadman, Andrew R. Haynes, Christine Heuston, Tracy Marsland, Anne Southwell, Pamel Trickett, Mark A. Strivens, Mark T. Ross, Wojciech Makalowski, Yixun Xu, Mark S. Boguski, Nigel P. Carter, Paul Denny, Steve D. M. Brown, Thomas J. Hudson and Eric S. Lander. Nature Genetics, 22(4):388 - 393, August, 1999.

A radiation hybrid map of the mouse genome. William J. Van Etten, Robert G. Steen, Huy Nguyen, Andrew B. Castle, Donna K. Slonim, Bing Ge, Chad Nusbaum, Greg D. Schuler, Eric S. Lander, and Thomas J. Hudson. Nature Genetics, 22(4):384 - 387, August, 1999.

A high-density integrated genetic linkage and radiation hybrid map of the laboratory rat. Robert G. Steen*, Anne E. Kwitek-Black*, Christopher Glenn*, Jo Gullings-Handley, William Van Etten, O. Scott Atkinson, Diane Appel, Simon Twigger, Melanie Muir, Tim Mull, Mary Granados, Mushira Kissebah, Kerri Russo, Robbin Crane, Michael Popp, Marc Peden, Tara Matise, Donna M. Brown, Jian Lu, Stephen Kingsmore, Peter J. Tonellato, Steve Rozen, Donna Slonim, Peter Young, Margit Knoblauch, Abraham Provoost, Detlev Ganten, Steven D. Colman, Jonathan Rothberg, Eric S. Lander, and Howard J. Jacob. Genome Research, 9:1046R, May, 1999.

A physical map of 30,000 human genes. Panos Deloukas, et al. Science, 282: 744-746, October, 1998.

Building human genome maps with radiation hybrids. Donna Slonim, Leonid Kruglyak, Lincoln Stein, and Eric Lander. Journal of Computational Biology, 4(4):487-504, December, 1997.

A gene map of the human genome. Greg Schuler, et al. Science, 274: 540-546, October, 1996.

An STS-based map of the human genome. Thomas J. Hudson, Lincoln D. Stein, Sebastian S. Gerety, Junli Ma, Andrew B. Castle, James Silva, Donna K. Slonim, Rafael Baptista, Leonid Kruglyak, Shu-Hua Xu, Xintong Hu, Angela M. E. Colbert, Carl Rosenberg, Mary Pat Reeve-Daly, Steve Rozen, Lester Hui, Xiaoyun Wu, Christina Vestergaard, Kimberly M. Wilson, Jane S. Bae, Shanak Maitra, Soula Ganiatsas, Cheryl A. Evans, Margaret M. DeAngelis, Kimberly A. Ingalls, Robert W. Nahf, Lloyd T. Horton Jr., Michele Oskin Anderson, Alville J. Collymore, Wenjuan Ye, Vardouhie Kouyoumjian, Irena S. Zemsteva, James Tam, Richard Devine, Dorothy F. Courtney, Michelle Turner Renaud, Huy Nguyen, Tara J. O'Connor, Cécile Fizames, Sabine Fauré, Gabor Gyapay, Colette Dib, Jean Morissette, James B. Orlin, Bruce W. Birren, Nathan Goodman, Jean Weissenbach, Trevor L. Hawkins, Simon Foote, David C. Page, and Eric S. Lander. Science, 270(5244): 1945-1954, December, 1995.

Computational Models of Machine Learning

Scalable FRaC Variants: Anomaly Detection for Precision Medicine. Cousins C, Pietras CM, Slonim DK. Proceedings of the HiComb Workshop on High Performance Comp. Biology, Orlando, FL, June 2017.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Keith Noto, Carla Brodley, and Donna Slonim. Data Mining and Knowledge Discovery, 25(1):109-133, 2012.

Anomaly detection using an ensemble of feature models. Keith Noto, Carla Brodley, and Donna Slonim. IEEE International Conference on Data Mining, pp 953-8, Sydney, Australia, Dec. 2010.

Learning with unreliable boundary queries. Avrim Blum, Prasad Chalasani, Sally Goldman, and Donna Slonim. Journal of Computer and System Sciences, 56:209-222, 1998.

Learning from Imperfect Data in Theory and Practice. Donna Slonim. Ph.D. Thesis, Massachusetts Institute of Technology. Available as Technical Report MIT/LCS/TR-670, June, 1996.

The power of team exploration: Two robots can learn unlabeled directed graphs. Michael Bender and Donna Slonim. Proceedings of the Thirty-Fifth Annual Symposium on Foundations of Computer Science (FOCS), 75-85, November, 1994.

Randomly fallible teachers: Learning monotone DNF with an incomplete membership oracle. Dana Angluin and Donna Slonim. Machine Learning, 14(1):7-26, January, 1994.

Miscellaneous publications in other areas

Putting benchmarks in their rightful place: the heart of computational biology. Peters B, Brenner S, Wang E, Slonim D, Kann M. PLoS Computational Biology, 14(11):e1006494, 2018.

Using Next-Generation Sequencing to Explore Genetics and Race in the High School Classroom. Yang X, Hartman MR, Harrington KT, Etson CM, Fierman MB, Slonim DK, Walt DR. CBE Life Sci Educ., Summer;16(2), 2017.

Personal microbiomes and next-generation sequencing for laboratory-based education. Hartman M, Harrington K, Etson C, Fierman M, Slonim D, Walt D. FEMS Microbiology Letters, Dec;363(23), 2016.

The Gene Ontology: enhancements for 2011. Blake JA, et al. Nucleic Acids Res. Jan;40(Database issue):D559-64, 2012.

A distance-based method for detecting horizontal gene transfer in whole genomes. Xintao Wei, Lenore Cowen, Carla Brodley, Arthur Brady, D. Sculley, and Donna K. Slonim International Symposium on Bioinformatics Research and Applications (ISBRA), 2008, pages 26-37.

Motif evaluation using leave-one-out scoring. Audrey Girouard, Noah Smith, and Donna Slonim. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), September, 2006.

* These authors contributed equally to this work.

Last updated Jan., 2022.