arXiv Machine Learning

Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution

arXiv:2607. 04987v1 Announce Type: new Abstract: Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology.

arXiv Machine Learning

Jul 3

A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation

arXiv:2607. 01307v1 Announce Type: new Abstract: NA methylation profiling has become a powerful approach for central nervous system (CNS) tumor classification, yet important challenges remain regarding cross-cohort transferability, methodological correctness, and robust multiclass evaluation.

By Paulo R. Ferreira Jr., Lucas Coutinho Freitas, La\'is dos Santos Gon\c{c}alves, William Borges Domingues, Lucas Petitemberte de Souza, Mariana B. Michalowski, Vinicius F. Campos

benchmarks

arXiv AI

Aug 5

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min--Max Selection

arXiv:2607. 19426v2 Announce Type: replace-cross Abstract: Large single-cell datasets are expensive to store, curate, and repeatedly reuse for model training.

By Yaodi Luo, Peize He, Lingbei Meng, Bowen Han, Zheng Lu, Jianqing Zhu, Lian Zhang

rag efficiency benchmarks

arXiv AI

Jul 23

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

arXiv:2607. 19426v1 Announce Type: cross Abstract: Single-cell datasets are increasingly costly to store, audit, and reuse for model training.

By Yaodi Luo, Peize He, Bowen Han, Lingbei Mengg

efficiency

arXiv Machine Learning

Jun 9

Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics

arXiv:2602. 15253v2 Announce Type: replace Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored.

By Ihor Kendiukhov

llms nlp

arXiv Machine Learning

Jun 9

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

arXiv:2606. 09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells.

By Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller, Manfredo Atzori, Barbara Di Camillo

llms rag safety

arXiv Machine Learning

Jun 2

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.

By Tianyang Xu, Tianci Liu, Niraj Rayamajhi, Ryan Patrick, Kranthi Varala, Ying Li, Jing Gao

llms multimodal

arXiv Machine Learning

Jun 19

Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection

arXiv:2606. 20174v1 Announce Type: new Abstract: Cell-free DNA (cfDNA) is a promising avenue for non-invasive multicancer early detection (MCED), in that, it can enable multiple cancer detection simultaneously from a single blood draw, with particular sensitivity to cancers that currently lack established screening programs.

By Nicko Starkey, Marcin W. Wojewodzic, Krzysztof Rzecki

multimodal safety

arXiv Machine Learning

Aug 4

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.

By Jiaqi Xiong, Yuntao hu, Yu Zheng, Yifei Shi, Xinyue Guo, Jiaxin Qi

arXiv AI

Aug 5

MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

arXiv:2607. 00385v2 Announce Type: replace-cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck.

By Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman

computer-vision

arXiv Machine Learning

Jul 21

GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures

arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?

By Kseniia Vaniushkina, Jeongmin Lim, Jinyong Park

llms rag safety

arXiv Machine Learning

2d ago

CytoBERT: A Foundation Model for Cytometry Data

arXiv:2608. 14414v1 Announce Type: new Abstract: Cytometry measures the complex characteristics of single cells (e.

By Syed Abdul Haseeb Qadri, Bjarne C. Hiller, Felix Blanke, Vanja Sophie Cangalovic, Kutalm{\i}\c{s} Co\c{s}kun, Amin Mirzaei, Tom Siegl, Sebastian Bader, Thomas Kirste, Martin Becker

fine-tuning

arXiv Machine Learning

Jun 5

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)

safety

Related stories