Social Media Ideas for Biological Data Interpretation

New AI Method Ranks Metabolites by Their Impact on Graph Neural Network Predictions

Metabolomics, the large-scale study of small molecules in biological systems, has long faced a stubborn interpretability problem. Researchers can measure thousands of metabolites a...

Robust circular cluster-based statistics for respiration-brain coupling

by Teresa Berther, Elio Balestrieri, Martina Saltafossi, Laura Bock Paulsen, Lau M. Andersen, Daniel S. Kluger The rapidly developing research field of brain-body neuroscience fac...

New AI approaches to help understand complex biological data

RNA sequencing combined with new AI models helps researchers analyze cellular relationships and biological patterns across scales, improving interpretation of complex single-cell a...

Balanced DNA interpolation improves learning of genetic distance-informed embeddings in plants

by Lara M. Kösters, Kevin Karbstein, Ladislav Hodač, Laura Albreht, Elvira Sahuquillo Balbuena, Daniel Botello, Olivier Hardy, Phebian Odufuwa, Eva Pardo Otero, Aireen Phang, Manue...

Opinion: AI-Driven Biology Is Only as Good as the Data Beneath It

The genomic data used to train AI models are often incomplete, contain errors, or lack contextual information. The post Opinion: AI-Driven Biology Is Only as Good as the Data Benea...

The multi-omics fallacy in microbiome science

by Rebecca Lewandowski Artificial intelligence and machine-learning-assisted multi-omics have expanded the scale and ambition of microbiome research, but they have also sharpened...

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data

by Robert Frank, Michael Widrich, Rahmad Akbar, Günter Klambauer, Geir Kjetil Sandve, Philippe A. Robert, Victor Greiff Generative machine learning models offer a powerful framewo...

When models choose metrics: Hidden geometry in computational biology

by Dillion M. Fox Computational biology often turns biological measurements into fitted models, embeddings, or summaries and then compares them with a distance chosen largely by c...

New Calibration Framework Improves Confidence in AI Virtual Cell Models

Across 14 genetic perturbation datasets and 18 evaluation metrics, commonly used measures such as mean squared error and control-referenced Pearson correlation were often poorly ca...

A Bayesian framework for multivariate differential analysis

by Marie Chion, Arthur Leroy Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the ma...

A real-data approach to evaluating RNA transcript analysis methods

RNA sequencing data provide a real-world framework for comparing differential transcript usage methods across bulk, single-cell, spatial, long-read, and short-read transcriptomics....

What Can Be Learned About Aging Clocks from Existing Clinical Trial Data for Longevity Interventions?

Aging clocks can be readily produced by using what are by now well established machine learning techniques on any body of biological data that changes with age. Omics data is favor...

Adding Low-Fidelity Data to ML Training Sets Could Yield Better Process Models

Low-fidelity trend information should be incorporated into the data sets used to train machine learning-based biopharmaceutical process models, according to new analysis, which sug...

CellDiffusion improves integration of single-cell RNA sequencing data across studies

CellDiffusion improves single-cell RNA sequencing data integration by correcting batch effects while preserving cell identities and developmental trajectories, enhancing clustering...

Machine learning and Voronoi-based decision boundaries for Bacterial vaginosis to determine population- specific microbi...

by Cameron G. Celeste, Carleigh C. Sokolik, Wambui Gachunga, Ivana K. Parker In this study we utilize machine learning techniques to create predictive models and determine key bac...

Divergent biological consequences of APOE isoforms across industrialized and non-industrial environments

by Marina M. Watowich, Rachel M. Petersen, Layla Brassington, Audrey M. Arner, Grace Rodenberg, Tan Bee Ting A/P Tan Boon Huat, Kar Lye Tam, Em Schellenberg, Izandis bin Mohd Sayed...

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data

by Hongjiang Guo, Tingfang Wu, Wenzheng Wang, Yelu Jiang, Geng Li, Liangpeng Nie, Yunhua Jia, Lijun Quan, Moli Huang, Qiang Lyu Precise resolution of cellular heterogeneity within...

<![CDATA[The Mechanics of Analyzing Linked Data Sets]]>

Interpreting Agreement in AI-Assisted American Society of Anesthesiologists (ASA) Classification

This letter comments on two aspects of the study by Lin et al. We discuss how agreement in ordinal ASA classification is reported and how disagreements across ASA classes could be...

Scientists characterize genetic variants across the human intrinsically disordered proteome

Variant effect prediction has entered a new phase, but one of biology’s largest blind spots remains stubbornly difficult to resolve. Computational tools have become increasingly ef...

For a Novice, Data Says More

рџђџ How the interpretation differsContinue reading on Medium В»

I Asked ChatGPT to Analyze 3 Datasets. It Made the Same Mistakes Every Time

The review pass fixed a row count and approved two wrong conclusions.

Multiomics in 2026 and Beyond

Multiomics analysis combines data from two or more omics layers—such as genomics, transcriptomics, proteomics, and epigenomics. However, published criticisms of multiomics approach...

Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing d...

by Scott J. Dos Santos, Andreea C. Murariu, Justin D. Silverman, Gregory B. Gloor Differential expression/abundance analyses are commonplace in studies employing high-throughput s...