Our fellows publish foundational advances in machine learning and new computational methods for biology in leading journals — and present their work at conferences around the world.
McGee, E. M., García de Albéniz, X., Eliassen, H., Yim, K., Dickerman, B., Preston, M., & Hernán, M.
(
2024
).
Target trial emulation of dynamic surveillance strategies for cancer survivors: An application to non-muscle invasive bladder cancer
Society for Epidemiologic Research (SER) Annual Meeting (Oral Presentation)
,
(
).
Lopes, E.W., McGee, E. M., Ananthakrishnan, A. N., Burke, K. E., Richter, J.E., Chan, A.T., & Khalili, H.
(
2024
).
Guideline-based Healthy Diet and Lifestyle Interventions for the Prevention of Crohn's Disease: A Target Trial Emulation
Digestive Disease Week
,
(
).
https://ddw.digitellinc.com/p/s/guideline-based-healthy-diet-and-lifestyle-interventions-for-the-prevention-of-crohns-disease-a-target-trial-emulation-6980Demetci, P., Huy Tran, Q., Redko, I. & Singh, R.
(
2024
).
Breaking isometric ties and introducing priors in Gromov-Wasserstein Distances
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics
,
Proceedings of Machine Learning Research 238:298-306
(
).
https://proceedings.mlr.press/v238/demetci24a/demetci24a.pdfDemirel, I., De Brouwer, E., M Hussain, Z., Oberst, M., A Philippakis, A. &; Sontag, D.
(
2024
).
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics
,
Proceedings of Machine Learning Research 238:4285-4293
(
).
https://proceedings.mlr.press/v238/demirel24a.htmlNazaret, A.O.R., Shi, C., & Blei, D
(
2024
).
On the Misspecification of Linear Assumptions in Synthetic Controls
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics
,
Proceedings of Machine Learning Research 238:3790-3798
(
).
https://proceedings.mlr.press/v238/nazaret24a.htmlXiong, K., Zhang, R., & Ma, J.
(
2024
).
scGHOST: Identifying single-cell 3D genome subcompartments.
Nature Methods.
,
(
).
https://doi.org/10.1038/s41592-024-02230-9Radhakrishnan, A., Beaglehole, D., Pandit, P., & Belkin, M.
(
2024
).
Mechanism for feature learning in neural networks and backpropagation-free machine learning models.
Science
,
383
(
6690
).
http://doi.org/10.1126/science.adi5639Ribot, A., Squires, C., & Uhler, C.
(
2024
).
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
3rd Conference on Causal Learning and Reasoning
,
Proceedings of Machine Learning Research vol 236:1–34
(
).
https://proceedings.mlr.press/v236/ribot24a/ribot24a.pdfCharpignon, M. L., Onofrey, S., Chen, Y. H., Rewegan, A., Glymour, M. M., Klevens, M., & Majumder, M.
(
2024
).
Association between social vulnerability and place of death during the first two years of COVID-19 in Massachusetts.
Age and Ageing
,
53
(
2
).
https://doi.org/10.1093/ageing/afae018Charpignon, M. L., Gupta, S., & Majumder, M.
(
2024
).
Massachusetts companion program bolsters COVID-19 vaccine rates among seniors.
Vaccines
,
42
(
3
).
https://doi.org/10.1016/j.vaccine.2023.12.048Rakic, M., Wong, H.E., Ortiz, J. J. G., Cimini, B., Guttag, J., & Dalca, A.V.
(
2024
).
Tyche: Stochastic In-context learning for medical imaging (Conference on Computer Vision and Pattern Recognition 2024)
,
(
).
https://arxiv.org/abs/2401.13650Wang, C., Gupta, S., Uhler, C., & Jaakkola, T.S.
(
2024
).
Removing Biases from Molecular Representation via Information Maximization
The Twelfth International Conference on Learning Representations
,
(
).
https://openreview.net/forum?id=7TOs9gjAg1Dai, H. , Ng, I., Luo, G., Spirtes, P., Stojanov, P., & Zhang, K.
(
2024
).
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
The Twelfth International Conference on Learning Representations
,
(
).
https://openreview.net/forum?id=gFR4QwK53hJain, A., Charpignon, M.-L., Chen, I. Y., Philippakis, A., & Alaa, A.
(
2024
).
Generating new drug repurposing hypotheses using disease-specific hypergraphs.
Proceedings of the Pacific Symposium on Biocomputing 2024.
,
(
).
https://doi.org/10.1142/9789811286421_0021DeVost, M. A., Chen, Y., Charpignon, M., Wells, W. M., Kiang, M. V., Mooney, A. C., Riley, A. R., Glymour, M. M., & Chen, R.
(
2023
).
Marital status associated with excess ADRD mortality among Californians during the COVID-19 pandemic.
Alzheimer’s Association International Conference
,
19
(
S22
).
https://doi.org/10.1002/alz.080634Brukhim, N., Dudik, M., Pacchiano, A., & Schapire, R.
(
2023
).
A Unified Model and Dimension for Interactive Estimation.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://openreview.net/pdf?id=iM0MWWBr4WJin, W., Sarkizova, S., Chen, X., Hacohen, N., & Uhler, C.
(
2023
).
Unsupervised protein-ligand binding energy prediction via neural Euler’s rotation equation.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://doi.org/10.48550/arXiv.2301.10814Kassraie, P., Emmenegger, N., Krause, A., & Pacchiano, A.
(
2023
).
Anytime model selection in linear bandits.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://arxiv.org/pdf/2306.14892.pdfLee, J. N., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., & Brunskill, E.
(
2023
).
Supervised pretraining can learn in-context reinforcement learning.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://doi.org/10.48550/arXiv.2306.14892Pacchiano, A., Lee, J. N., & Brunskill, E.
(
2023
).
Experiment planning with function approximation.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://openreview.net/pdf?id=axmY49ahVIShiragur, K., Zhang, J., & Uhler, C.
(
2023
).
Meek separators and their applications in targeted causal discovery.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://doi.org/10.48550/arXiv.2310.20075Braunger, J. M., Cammarata, L. V., Sornapudi, T. R., Uhler, C., & Shivashankar, G. V.
(
2023
).
Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging.
Aging Cell
,
23
(
3
).
https://doi.org/10.1111/acel.14056Chaung, K., Baharav, T. Z., Henderson, G., Zheludev, I. N., Wang, P. L., & Salzman, J.
(
2023
).
SPLASH: A statistical, reference-free genomic algorithm unifies biological discovery.
Cell
,
186
(
25
).
https://doi.org/10.1016/j.cell.2023.10.028Nazaret, A., Tonekaboni, S., Darnell, G., Ren, S. Y., Sapiro, G., & Miller, A. C.
(
2023
).
Modeling personalized heart rate response to exercise and environmental factors with wearables data.
Npj Digital Medicine
,
6
(
1
).
https://doi.org/10.1038/s41746-023-00926-4Zhang, J., Squires, C., Greenewald, K., Srivastava, A., Shanmugam, K., & Uhler, C.
(
2023
).
Identifiability guarantees for causal disentanglement from soft interventions.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://doi.org/10.48550/arXiv.2307.06250Parres-Gold, J., Levine, M., Emert, B., Stuart, A., & Elowitz, M. B.
(
2023
).
Principles of computation by competitive protein dimerization networks.
bioRxiv.
,
(
).
https://doi.org/10.1101/2023.10.30.564854Charpignon, M.-L., Carrel, A., Jiang, Y., Kwaga, T., Cantada, B., Hyslop, T., Cox, C. E., Haines, K., Koomson, V., Dumas, G., Morley, M., Dunn, J., & Ian Wong, A.-K.
(
2023
).
Going beyond the means: Exploring the role of bias from digital determinants of health in technologies.
PLOS Digital Health
,
2
(
10
).
https://doi.org/10.1371/journal.pdig.0000244Matos, J., Struja, T., Gallifant, J., Nakayama, L., Charpignon, M.-L., Liu, X., Economou-Zavlanos, N., Cardoso, J. S., Johnson, K. S., Bhavsar, N., Gichoya, J., Celi, L. A., & Wong, A. I.
(
2023
).
BOLD: Blood-gas and oximetry linked dataset – open source research.
bioRxiv.
,
(
).
https://doi.org/10.1101/2023.10.03.23296485Deng, Y., Zhang, R., Xu, P., Ma, J., & Gu, Q.
(
2023
).
PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning.
bioRxiv.
,
(
).
https://doi.org/10.1101/2023.10.01.560404Zhang, J., Cammarata, L., Squires, C., Sapsis, T. P., & Uhler, C.
(
2023
).
Active learning for optimal intervention design in causal models.
Nature Machine Intelligence
,
5
(
10
).
https://doi.org/10.1038/s42256-023-00719-0Charpignon, M.-L., Byers, J., Cabral, S., Celi, L. A., Fernandes, C., Gallifant, J., Lough, M. E., Mlombwa, D., Moukheiber, L., Ong, B. A., Panitchote, A., William, W., Wong, A.-K. I., & Nazer, L.
(
2023
).
Critical bias in critical care devices.
Critical Care Clinics
,
39
(
4
).
https://doi.org/10.1016/j.ccc.2023.02.005Nazaret, A., & Sapiro, G.
(
2023
).
A large-scale observational study of the causal effects of a behavioral health nudge.
Science Advances
,
9
(
38
).
https://doi.org/10.1126/sciadv.adi1752Moran, G. E., Blei, D. M., & Ranganath, R.
(
2023
).
Holdout predictive checks for Bayesian model criticism.
Journal of the Royal Statistical Society. Series B, Statistical Methodology
,
86
(
1
).
https://doi.org/10.1093/jrsssb/qkad105Radhakrishnan, A., Ruiz Luyten, M., Prasad, N., & Uhler, C.
(
2023
).
Transfer learning with kernel methods.
Nature Communications
,
14
(
1
).
https://doi.org/10.1038/s41467-023-41215-8Nguyen, D. S. A., Charpignon, M.-L., Schaber, K. L., Majumder, M. S., & Perrault, A.
(
2023
).
Risk-based ring vaccination: A strategy for pandemic control and vaccine allocation.
29TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
,
(
).
https://openreview.net/pdf?id=N0qlvDjnEvChoo, D., & Shiragur, K.
(
2023
).
Adaptivity complexity for causal graph discovery.
39th Conference on Uncertainty in Artificial Intelligence.
,
(
).
https://doi.org/10.48550/arXiv.2306.05781Squires, C., Seigal, A., Bhate, S., & Uhler, C.
(
2023
).
Linear causal disentanglement via interventions.
Proceedings of the 40th International Conference on Machine Learning (ICML 2023).
,
(
).
https://dl.acm.org/doi/10.5555/3618408.3619756Chen, R., Charpignon, M.-L., Raquib, R. V., Wang, J., Meza, E., Aschmann, H. E., DeVost, M. A., Mooney, A., Bibbins-Domingo, K., Riley, A. R., Kiang, M. V., Chen, Y.-H., Stokes, A. C., & Glymour, M. M.
(
2023
).
Excess mortality with Alzheimer disease and related dementias as an underlying or contributing cause during the COVID-19 pandemic in the US.
JAMA Neurology
,
80
(
9
).
https://doi.org/10.1001/jamaneurol.2023.2226Agrawal, R., Squires, C., Prasad, N., & Uhler, C.
(
2023
).
The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables.
Journal of the Royal Statistical Society. Series B, Statistical Methodology
,
85
(
5
).
https://doi.org/10.1093/jrsssb/qkad071Jana, S., Polyanskiy, Y., Teh, A., & Wu, Y.
(
2023
).
Empirical Bayes via ERM and Rademacher complexities: the Poisson model.
36th Annual Conference on Learning Theory.
,
(
).
https://doi.org/10.48550/arXiv.2307.02070Viippola, E., Kuitunen, S., Rodosthenous, R. S., Vabalas, A., Hartonen, T., Vartiainen, P., Demmler, J., Vuorinen, A.-L., Liu, A., Havulinna, A. S., Llorens, V., Detrois, K. E., Wang, F., Ferro, M., Karvanen, A., German, J., Jukarainen, S., Gracia-Tabuenca, J., Hiekkalinna, T., … FinnGen.
(
2023
).
Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry).
International Journal of Epidemiology
,
52
(
4
).
https://doi.org/10.1093/ije/dyad091Matos, J., Struja, T., Gallifant, J., Charpignon, M.-L., Cardoso, J. S., & Celi, L. A.
(
2023
).
Shining light on dark skin: Pulse oximetry correction models.
2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG).
,
(
).
http://10.1109/ENBENG58165.2023.10175316Choo, D., & Shiragur, K.
(
2023
).
New metrics and search algorithms for weighted causal DAGs.
40th International Conference on Machine Learning.
,
(
).
https://doi.org/10.48550/arXiv.2305.04445Lundberg, D. J., Wrigley-Field, E., Cho, A., Raquib, R., Nsoesie, E. O., Paglino, E., Chen, R., Kiang, M. V., Riley, A. R., Chen, Y.-H., Charpignon, M.-L., Hempstead, K., Preston, S. H., Elo, I. T., Glymour, M. M., & Stokes, A. C.
(
2023
).
COVID-19 mortality by race and ethnicity in US metropolitan and nonmetropolitan areas, March 2020 to February 2022.
JAMA Network Open
,
6
(
5
).
https://doi.org/10.1001/jamanetworkopen.2023.11098Gal, E., Singh, S., Pacchiano, A., Walker, B., Lyons, T., & Foerster, J.
(
2023
).
Unbiased decisions reduce regret: Adversarial domain adaptation for the bank loan problem.
International Conference on Learning Representations 2023.
,
(
).
https://doi.org/10.48550/arXiv.2308.08051Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., & Uhler, C.
(
2023
).
Cross-modal autoencoder framework learns holistic representations of cardiovascular state.
Nature Communications
,
14
(
1
).
https://doi.org/10.1038/s41467-023-38125-0Choo, D., & Shiragur, K.
(
2023
).
Subset verification and search algorithms for causal DAGs.
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS).
,
(
).
https://proceedings.mlr.press/v206/choo23a/choo23a.pdfHussain, Z., Shih, M.-C., Oberst, M., Demirel, I., & Sontag, D.
(
2023
).
Falsification of internal and external validity in observational studies via Conditional Moment Restrictions.
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics.
,
(
).
https://proceedings.mlr.press/v206/hussain23a/hussain23a.pdfPaireau, J., Charpignon, M.-L., Larrieu, S., Calba, C., Hozé, N., Boëlle, P.-Y., Thiebaut, R., Prague, M., & Cauchemez, S.
(
2023
).
Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France.
BMC Infectious Diseases
,
23
(
1
).
https://doi.org/10.1186/s12879-023-08106-1Radhakrishnan, A., Belkin, M., & Uhler, C.
(
2023
).
Wide and deep neural networks achieve consistency for classification.
Proceedings of the National Academy of Sciences of the United States of America
,
120
(
14
).
https://doi.org/10.1073/pnas.2208779120Glymour, M. M., Charpignon, M.-L., Chen, Y.-H., & Kiang, M. V.
(
2023
).
Counterpoint: Preprints and the future of scientific publishing-in favor of relevance.
American Journal of Epidemiology
,
192
(
7
).
https://doi.org/10.1093/aje/kwad052Ramjee, D., Pollack, C. C., Charpignon, M.-L., Gupta, S., Rivera, J. M., El Hayek, G., Dunn, A. G., Desai, A. N., & Majumder, M. S.
(
2023
).
Evolving face mask guidance during a pandemic and potential harm to public perception: Infodemiology study of sentiment and emotion on Twitter.
Journal of Medical Internet Research
,
25.
(
).
https://doi.org/10.2196/40706Mayer, A. T., Holman, D. R., Sood, A., Tandon, U., Bhate, S. S., Bodapati, S., Barlow, G. L., Chang, J., Black, S., Crenshaw, E. C., Koron, A. N., Streett, S. E., Gambhir, S. S., Sandborn, W. J., Boland, B. S., Hastie, T., Tibshirani, R., Chang, J. T., Nolan, G. P., … Rogalla, S.
(
2023
).
A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy.
Science Advances
,
9
(
3
).
https://doi.org/10.1126/sciadv.add1166Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., Juan, Y.-H., Yang, C.-T., Wan, Y.-L., Lin, G., Sequist, L. V., Fintelmann, F. J., & Barzilay, R.
(
2023
).
Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography.
Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology
,
41
(
12
).
https://doi.org/10.1200/JCO.22.01345Chewi, S., Gerber, P., Lee, H., & Lu, C.
(
2023
).
Fisher information lower bounds for sampling.
34th International Conference on Algorithmic Learning Theory.
,
(
).
https://proceedings.mlr.press/v201/chewi23b/chewi23b.pdfSturma, N., Squires, C., Drton, M., & Uhler, C.
(
2023
).
Unpaired multi-domain causal representation learning.
Advances in Neural Information Processing Systems (NeurIPS 2023).
,
(
).
https://openreview.net/pdf?id=zW1uVN6MbvVakulenko-Lagun, B., Magdamo, C., Charpignon, M.-L., Zheng, B., Albers, M. W., & Das, S.
(
2023
).
causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data.
Computer Methods and Programs in Biomedicine
,
242.
(
).
https://doi.org/10.1016/j.cmpb.2023.107819Wang, T. T., Zablotchi, I., Shavit, N., & Rosenfeld, J. S.
(
2023
).
Cliff-Learning.
arXiv
,
(
).
https://doi.org/10.48550/arXiv.2302.07348Wang, T., Zeleznik, O., McGee, E. E., Brantley, K. D., Balasubramanian, R., Rosner, B. A., Willett, W. C., Clish, C. B., & Eliassen, A. H.
(
2023
).
Abstract 3022: Prospective study of circulating metabolomic signatures and breast cancer incidence among predominantly premenopausal women.
Cancer Research
,
83
(
7_Supplement
).
https://doi.org/10.1158/1538-7445.am2023-3022Zhou, T., Zhang, R., Jia, D., Doty, R. T., Munday, A. D., Gao, D., Xin, L., Abkowitz, J. L., Duan, Z., & Ma, J.
(
2023
).
Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells.
bioRxiv.
,
(
).
https://doi.org/10.1101/2023.07.20.549578Charpignon, M.-L., Vakulenko-Lagun, B., Zheng, B., Magdamo, C., Su, B., Evans, K., Rodriguez, S., Sokolov, A., Boswell, S., Sheu, Y.-H., Somai, M., Middleton, L., Hyman, B. T., Betensky, R. A., Finkelstein, S. N., Welsch, R. E., Tzoulaki, I., Blacker, D., Das, S., & Albers, M. W.
(
2022
).
Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.
Nature Communications
,
13
(
1
).
https://doi.org/10.1038/s41467-022-35157-wMallinar, N., Simon, J. B., Abedsoltan, A., Pandit, P., Belkin, M., & Nakkiran, P.
(
2022
).
Benign, tempered, or catastrophic: A taxonomy of overfitting.
Advances in Neural Information Processing Systems (NeurIPS 2022).
,
(
).
https://doi.org/10.48550/arXiv.2207.06569Wong, A.-K. I., Kim, H., Charpignon, M.-L., Carvalho, L., Monares-Zepeda, E., Madushani, R. W. M. A., Adhikari, L., Kindle, R. D., Kutner, M., Celi, L. A., Lough, M. E., & Mireles-Cabodevila, E.
(
2022
).
A method to explore variations of ventilator-associated event surveillance definitions in large critical care databases in the United States.
Critical Care Explorations
,
4
(
11
).
https://doi.org/10.1097/CCE.0000000000000790Yuan, L., Roy, B., Ratna, P., Uhler, C., & Shivashankar, G. V.
(
2022
).
Lateral confined growth of cells activates Lef1 dependent pathways to regulate cell-state transitions.
Scientific Reports
,
12
(
1
).
https://doi.org/10.1038/s41598-022-21596-4Bührer, E. D., Amrein, M. A., Forster, S., Isringhausen, S., Schürch, C. M., Bhate, S. S., Brodie, T., Zindel, J., Stroka, D., Sayed, M. A., Nombela-Arrieta, C., Radpour, R., Riether, C., & Ochsenbein, A. F.
(
2022
).
Splenic red pulp macrophages provide a niche for CML stem cells and induce therapy resistance.
Leukemia
,
36
(
11
).
https://doi.org/10.1038/s41375-022-01682-2Squires, C., & Uhler, C.
(
2022
).
Causal structure learning: A combinatorial perspective.
Foundations of Computational Mathematics
,
23.
(
).
https://doi.org/10.1007/s10208-022-09581-9Jin, W., Barzilay, R., & Jaakkola, T.
(
2022
).
Antibody-antigen docking and design via hierarchical equivariant refinement.
Proceedings of the 39th International Conference on Machine Learning.
,
(
).
https://doi.org/10.48550/arXiv.2207.06616Squires, C., Yun, A., Nichani, E., Agrawal, R., & Uhler, C.
(
2022
).
Causal structure discovery between clusters of nodes induced by latent factors.
First Conference on Causal Learning and Reasoning (CLeaR).
,
(
).
https://proceedings.mlr.press/v177/squires22a/squires22a.pdfUhler, C., & Shivashankar, G. V.
(
2022
).
Machine learning approaches to single-cell data integration and translation.
Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
,
110
(
5
).
https://doi.org/10.1109/jproc.2022.3166132Radhakrishnan, A., Stefanakis, G., Belkin, M., & Uhler, C.
(
2022
).
Simple, fast, and flexible framework for matrix completion with infinite width neural networks.
Proceedings of the National Academy of Sciences of the United States of America
,
119
(
16
).
https://doi.org/10.1073/pnas.2115064119Vlachas, P. R., Arampatzis, G., Uhler, C., & Koumoutsakos, P.
(
2022
).
Multiscale simulations of complex systems by learning their effective dynamics.
Nature Machine Intelligence
,
4
(
4
).
https://doi.org/10.1038/s42256-022-00464-wSquires, C., Shen, D., Agarwal, A., Shah, D., & Uhler, C.
(
2022
).
Causal Imputation via Synthetic Interventions.
First Conference on Causal Learning and Reasoning (CLeaR).
,
(
).
https://proceedings.mlr.press/v177/squires22b/squires22b.pdfMoran, G. E., Sridhar, D., Wang, Y., & Blei, D. M.
(
2022
).
Identifiable deep generative models via sparse decoding.
Transactions on Machine Learning Research.
,
(
).
https://doi.org/10.48550/arXiv.2110.10804Navarro, M., Wang, Y., Marques, A. G., Uhler, C., & Segarra, S.
(
2022
).
Joint inference of multiple graphs from matrix polynomials.
The Journal of Machine Learning Research
,
23
(
1
).
https://www.jmlr.org/papers/volume23/20-1375/20-1375.pdfAlaa, A., Philippakis, A., & Sontag, D.
(
2022
).
ETAB: A benchmark suite for visual representation learning in echocardiography.
36th Conference on Neural Information Processing Systems (NeurIPS 2022).
,
(
).
https://openreview.net/pdf?id=b0VDQiNLPy9Belyaeva, A., Kubjas, K., Sun, L. J., & Uhler, C.
(
2022
).
Identifying 3D genome organization in diploid organisms via Euclidean distance geometry.
SIAM Journal on Mathematics of Data Science
,
4
(
1
).
https://doi.org/10.1137/21m1390372Bhate, S. S., Seigal, A., & Caicedo, J.
(
2022
).
Deciphering causal genomic templates of complex molecular phenotypes.
bioRxiv.
,
(
).
https://doi.org/10.1101/2022.08.15.503769Jin, W., Wohlwend, J., Barzilay, R., & Jaakkola, T.
(
2022
).
Iterative refinement graph neural network for antibody sequence-structure co-design.
International Conference on Learning Representations 2022.
,
(
).
https://www.ericandwendyschmidtcenter.org/updates/eric-and-wendy-schmidt-center-fellows-develop-ai-methods-to-design-antibodies-and-virtually-screen-drugsLee, M. Y., Bedia, J. S., Bhate, S. S., Barlow, G. L., Phillips, D., Fantl, W. J., Nolan, G. P., & Schürch, C. M.
(
2022
).
CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.
BMC Bioinformatics, 23(1).
,
(
).
https://doi.org/10.1186/s12859-022-04570-9Zhang, X., Wang, X., Shivashankar, G. V., & Uhler, C.
(
2022
).
Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease.
Nature Communications
,
13
(
1
).
https://doi.org/10.1038/s41467-022-35233-1
