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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

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

Sep 23

An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Prediction

arXiv:2609.25088v1 Announce Type: cross Abstract: Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these ob...

By Mushahid Intesum

multimodal safety

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 Computer Vision

Aug 25

Exploring learning environments for label\-efficient cancer diagnosis

arXiv:2408.07988v3 Announce Type: replace Abstract: Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial fo...

By Samta Rani, Tanvir Ahmad, Sarfaraz Masood, Chandni Saxena

llms

arXiv AI

Sep 10

The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]

The study evaluates the use of default decision thresholds (t=0.50) in multi‑label enzyme commission (EC) number prediction across 14,096 compounds and six EC classes. It finds a high mean accuracy of 77.16% but low macro F1 (0.3976) and macro recall (0.3872), indicating severe class‑imbalance issues: majority classes are over‑predicted while minority classes, especially EC6, have zero recall despite reasonable ROC‑AUC. The authors recommend target‑specific threshold tuning and conformal calibration as post‑processing safeguards to expose and correct these hidden errors.

By Bilal Ahmad, Rajed Mehmood

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

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