| A |
1 |
Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels |
Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet, Carola-Bibiane Schönlieb, Riku Turkki, Yinhai Wang |
| A |
2 |
Biological Neurons vs. Deep Reinforcement Learning: Sample efficiency in a simulated game-world |
Forough Habibollahi, Moein Khajehnejad, Amitesh Gaurav, Brett J. Kagan |
| A |
3 |
Learning More Effective Cell Representations Efficiently |
Jason Xiaotian Dou, Minxue Jia, Nika Zaslavsky, Mark Ebeid, Runxue Bao, Shiyi Zhang, Ke Ni, Paul Pu Liang, Haiyi Mao, Zhi-Hong Mao |
| A |
4 |
What cleaves? Is proteasomal cleavage prediction reaching a ceiling? |
Ingo Ziegler, Bolei Ma, Ercong Nie, Bernd Bischl, David Rügamer, Benjamin Schubert, Emilio Dorigatti |
| A |
5 |
CoSpar identifies early cell fate biases from single cell transcriptomic and lineage information |
Shou-Wen Wang, Michael J Herriges, Kilian Hurley, Darrell N. Kotton, Allon M. Klein |
| A |
6 |
Designing and Evolving Neuron-Specific Proteases |
Han Spinner, Colin Hemez, Julia McCreary, David Liu, Debora Marks |
| A |
7 |
Spatially-aware dimension reduction of transcriptomics data |
Lauren Okamoto, Andrew Jones, Archit Verma, Barbara E. Engelhardt |
| A |
8 |
Box Prediction Rebalancing for Training Single-Stage Object Detectors with Partially Labeled Data |
Shafin Haque, R. Austin McEver |
| A |
9 |
Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data |
Utku Ozbulak |
| A |
10 |
scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data |
Tessa Durakis Green, Stefan Peidli, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Jake P. Taylor-King, Debora S. Marks, Augustin Luna, Nils Blüthgen, Chris Sander |
| A |
11 |
Kernelized Stein Discrepancies for Biological Sequences |
Alan N Amin, Eli N Weinstein, Debora S Marks |
| A |
12 |
3D single-cell shape analysis of cancer cells using geometric deep learning |
Matt De Vries, Lucas G Dent, Nathan Curry, Leo Rowe-Brown, Adam Tyson, Christopher Dunsby, Chris Bakal |
| A |
13 |
Using hierarchical variational autoencoders to incorporate conditional independent priors for paired single-cell multi-omics data integration |
Ping-Han Hsieh, Ru-Xiu Hsiao, Tatiana Belova, Katalin Ferenc, Anthony Mathelier, Rebekka Burkholz, Chien-Yu Chen, Geir Kjetil Sandve, Marieke Lydia Kuijjer |
| A |
14 |
Translating L-peptides into non-canonical linear and macrocyclic peptides |
Somesh Mohapatra |
| A |
15 |
Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis |
Xiangling Liu, Xinyu Yang, Linkun Ouyang, Guibing Guo, Jin Su, Ruibin Xi, Ke Yuan, Fajie Yuan |
| A |
16 |
Deep Fitness Inference for Drug Discovery with Directed Evolution |
Nathaniel Lee Diamant, Ziqing Lu, Christina Helmling, Kangway V Chuang, Christian Cunningham, Tomasso Biancalini, Gabriele Scalia, Max W. Shen |
| B |
17 |
LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly |
Srivatsa Kundurthy |
| B |
18 |
Generative model for Pseudomonad genomes |
Manasa Kesapragada, R Shane Canon, Sean P Jungbluth, Marcin P Joachimiak, Adam P Arkin, Paramvir S Dehal |
| B |
19 |
Learning relationships between histone modifications in single cells |
Jake Yeung, Maria Florescu, Peter Zeller, Buys de Barbanson, Max D Wellenstein, Alexander van Oudenaarden |
| B |
20 |
Fuzzy Logic for Biological Networks as ML Regression: Scaling to Single-Cell Datasets with Autograd |
Constance Le Gac, Alice Driessen, Nicolas Deutschmann, María Rodríguez Martínez |
| B |
21 |
MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport |
Federico Gossi, Pushpak Pati, Adriano Martinelli, Maria Anna Rapsomaniki |
| B |
22 |
Transformer Model for Genome Sequence Analysis |
Noah Hurmer, Xiao-Yin To, Martin Binder, Hüseyin Anil Gündüz, Philipp C. Münch, René Mreches, Alice C McHardy, Bernd Bischl, Mina Rezaei |
| B |
23 |
decOM: Similarity-based microbial source tracking of ancient oral samples using k-mer-based methods |
Camila Duitama Gonzalez, Riccardo Vicedomini, Téo Lemane, Nicolás Rascovan, Hugues Richard, Rayan Chikhi |
| B |
24 |
Double trouble: Predicting new variant counts across two heterogeneous populations |
Yunyi Shen, Lorenzo Masoero, Joshua Schraiber, Tamara Broderick |
| B |
25 |
Forecasting labels under distribution-shift for machine-guided sequence design |
Lauren Berk Wheelock, Stephen Malina, Jeffrey Gerold, Sam Sinai |
| B |
26 |
Learning representations of cell populations for image-based profiling using contrastive learning |
Robert van Dijk, John Arevalo, Mehrtash Babadi, Anne E. Carpenter, Shantanu Singh |
| B |
27 |
MolE: a molecular foundation model for drug discovery |
Oscar Mendez-Lucio, Christos A Nicolaou, Berton Earnshaw |
| B |
28 |
meTCRs - Learning a metric for T-cell receptors |
Felix Drost, Lennard Schiefelbein, Benjamin Schubert |
| B |
29 |
Knowledge distillation for fast and accurate DNA sequence correction |
Anastasiya Belyaeva*, Joel Shor*, Daniel E. Cook, Kishwar Shafin, Daniel Liu, Armin Töpfer, Aaron M. Wenger, William J. Rowell, Howard Yang, Alexey Kolesnikov, Cory Y. McLean, Maria Nattestad, Andrew Carroll, Pi-Chuan Chang |
| B |
30 |
CP2Image: Generating high-quality single-cell images using CellProfiler representations |
Yanni Ji, Marie F.A. Cutiongco, Bjørn Sand Jensen, Ke Yuan |
| B |
31 |
Standards, tooling and benchmarks to probe representation learning on proteins |
Joaquin Gomez Sanchez, Sebastian Franz, Michael Heinzinger, Christian Dallago, Burkhard Rost |
| B |
32 |
A single-cell gene expression language model |
William Connell, Umair Khan, Michael Keiser |
| C |
33 |
ChromFormer: A transformer-based model for 3D genome structure prediction |
Henry Valeyre, Pushpak Pati, Federico Gossi, Vignesh Ram Somnath, Adriano Martinelli, Maria Anna Rapsomaniki |
| C |
34 |
Find your microenvironments faster with Neural Spatial LDA |
Sivaramakrishnan Sankarapandian, Jun Xu, Zhenghao Chen |
| C |
35 |
Characterization of rare transitory cell-statesfrom single-cell data with Mellon |
Dominik Jenz Otto, Manu Setty, Brennan Dury |
| C |
36 |
EpiAttend: A transformer model of gene regulation combining single cell epigenomes with DNA sequence |
Russell Li, Heng Xu, Eran Mukamel |
| C |
37 |
An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic Discovery |
Bo Yuan, Cory Y McLean, Farhad I Hormozdiari, Justin Cosentino |
| C |
38 |
Machine Learning enabled Pooled Optical Screening in Human Lung Cancer Cells |
Srinivasan Sivanandan, Max Salick, Bobby Leitmann, Kara Marie Liu, Mohammad Sultan, Navpreet Ranu, Cynthia Vivian Hao, Owen Chen, John Bisognano, Eric Lubeck, Ajamete Kaykas, Eilon Sharon, Ci Chu |
| C |
39 |
Interpretable visualization of single cell data using Janus autoencoders |
Gokul Gowri, Philippa Richter, Xiao-Kang Lun, Peng Yin |
| C |
40 |
Designing active and thermostable enzymes with sequence-only predictive models |
Clara Fannjiang, Micah Olivas, Eric R. Greene, Craig J. Markin, Bram Wallace, Ben Krause, Margaux M. Pinney, James S. Fraser, Polly M. Fordyce, Ali Madani, Nikhil Naik |
| C |
41 |
Regression-Based Elastic Metric Learning on Shape Spaces of Cell Curves |
Adele Myers, Nina Miolane |
| C |
42 |
Biological Cartography: Building and Benchmarking Representations of Life |
Safiye Celik, Jan-Christian Hütter, Sandra Melo Carlos, Nathan H. Lazar, Rahul Mohan, Conor Tillinghast, Tommaso Biancalani, Marta Fay, Berton A. Earnshaw, Imran S. Haque |
| C |
43 |
Modeling Single-cell Dynamics using Unbalanced Parameterized Monge Maps |
Luca Eyring, Dominik Klein, Giovanni Palla, Sören Becker, Philipp Weiler, Niki Kilbertus and Fabian Theis |
| C |
44 |
A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction |
Krzysztof Koras, Marcin Możejko, Paulina Szymczak, Adam Izdebski, Eike Staub, Ewa Szczurek |
| C |
45 |
Personalised drug recommendation from augmented gene expression data - the right drug(s) for the right patient |
Manuela Salvucci, Davy Suvee, Deniz Pirincci, Dimitrius Raphael, Eryk Kropiwnicki, Giovanni Dall’olio, James O’Reilly, Katie Sanford, Marika Catapano, Marta Sarrico, Xenia Galkina, Francesca Mulas |
| C |
46 |
Adding prior information to data integration pipelines improves performance on downstream tasks and facilitates data interpretation |
Sara Masarone |
| C |
47 |
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings |
Frederike Lübeck, Charlotte Bunne, Gabriele Gut, Jacobo Sarabia del Castillo, Lucas Pelkmans, David Alvarez-Melis |
| C |
48 |
TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction |
Pascal Notin, Lood Van Niekerk, Aaron W Kollasch, Daniel Ritter, Yarin Gal, Debora S Marks |
| C |
49 |
Multimodal Cell-Free DNA Embeddings are Informative for Early Cancer Detection |
Felix Jackson, Jingfei Cheng, Masato Inoue, Chunxiao Song |
| B |
50 |
Energy-based Modelling for Single-cell Data Annotation |
Tianyi Liu , Philip Fradkin, Lazar Atanackovic, Leo J Lee |
| B |
51 |
Is brightfield all you need for MoA prediction? |
Ankit Gupta, Philip John Harrison, Håkan Wieslander, Jonne Rietdijk, Jordi Carreras Puigvert, Polina Georgiev, Carolina Wahlby, Ola Spjuth, Ida-Maria Sintorn |