36 citations · 59 across the 10 of their papers we have counts for
11 papers
OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
Maciej Sypetkowski, Joanna Krawczyk, Łukasz Smoliński +4
Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profiles without producing natural-…
BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine
Remigiusz Kinas, Joanna Krawczyk, Rafał Powalski +6
Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative multi-omics analysis while pre…
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Philip Fradkin, Puria Azadi, Karush Suri +4
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based…
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +14
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores…
On the Scalability of GNNs for Molecular Graphs
Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei +4
Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Masked Autoencoders are Scalable Learners of Cellular Morphology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +13
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results…
Spatial Latent Representations in Generative Adversarial Networks for Image Generation
Maciej Sypetkowski
In the majority of GAN architectures, the latent space is defined as a set of vectors of given dimensionality. Such representations are not easily interpretable and do not capture…
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian +9
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
Augmentation Inside the Network
Maciej Sypetkowski, Jakub Jasiulewicz, Zbigniew Wojna
In this paper, we present augmentation inside the network, a method that simulates data augmentation techniques for computer vision problems on intermediate features of a convoluti…