arXiv Machine Learning July 3, 2026 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

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jul 7

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.

By Dmytro Rizdvanetskyi, Nathan Ross, Pavlo Lutsik

benchmarks

arXiv Machine Learning

Jul 14

Generative Augmentation of Raman Spectra for Glioma Classification

arXiv:2607. 10196v1 Announce Type: new Abstract: Access to sufficiently large biomedical datasets remains a major obstacle for machine learning in Raman spectroscopy-based diagnostics.

By Andrei Iu\c{s}an, Iulian Vasile, Daria Voiculescu, Ion Petre, Andrei P\u{a}un, Bogdan Oancea, Mihaela P\u{a}un

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

Jul 24

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.

By Afsaneh Mahanipour, Hana Khamfroush

computer-vision benchmarks safety

arXiv AI

Jul 7

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.

By Chama Bensmail

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arXiv Machine Learning

Aug 11

TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification

arXiv:2606. 09898v2 Announce Type: replace Abstract: Cancer treatment involves decisions across multiple clinical outcomes, yet pathway-informed deep learning models are typically evaluated in isolation, making their relative benefits unclear.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy

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

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