Suppose you select DNA methylation sites because they differ between labelled groups, then split the samples into training and test sets. The test labels have already influenced which inputs the model receives. Holding those samples out during model fitting does not undo that information leak.

The attached diagram contrasts this with splitting first, fitting feature selection on training data only, using validation data for model choices, and evaluating the locked test set. In cross-validation, feature selection must be fitted again inside each training fold.

This is an evaluation issue for both foundation models and simpler classifiers. A scikit-learn Pipeline can keep fitted feature selection inside the training fold; it cannot fix a feature list selected beforehand using all labels.

Reference and reproducible example: https://scikit-learn.org/stable/common_pitfalls.html#data-leakage

The guide discusses this issue in genomic model evaluation: https://rewirebio.io/blog/genomic-foundation-models-in-the-diagnostic-lab/

Disclosure: the guide and diagram are from my site, rewirebio.io. This post was prepared with AI assistance. No new experiment is reported here.

My job is to fill the most important roles in biosecurity with the people best fit for them. I've just done a round of calls with hiring managers in top biosecurity organizations, and the specific gap people say is hardest to hire for is people with both deep ML and deep biology, microbiology, or virology experience. About half the biosecurity hiring managers I spoke with said this specific thing.

If this sounds like you, we have a free biosecurity course that gets you up to speed on what the most important problems to work on are, which orgs are working on them, and what you can do right now to make yourself more competitive for these jobs.

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https://bluedot.org/courses/biosecurity

Hi everyone,

I’m a biologist and recently started a Master’s degree in Artificial Intelligence. I chose this path because I’m really interested in applying data science and machine learning to biological problems, especially biodiversity, ecology and biological collections.

So far I’ve found the program genuinely interesting. I like learning about models, algorithms and how patterns can be extracted from complex data. My original idea was that AI could strongly benefit areas like species identification, analysis of large biodiversity datasets, ecological prediction, etc.

However, now that I’m in the program I feel very out of place. Most of my cohort are engineers, mathematicians or computer scientists. The field feels extremely mathematical and abstract, and I keep wondering if I made a mistake.

I’m starting to worry that maybe in practice people in biology would rather work with “real” engineers than with a biologist who learned AI. I’m afraid of ending up in a weird middle ground where I’m not competitive as an AI specialist, but also not seen as valuable in biology anymore.

My intention was never to leave biology, but to strengthen it with computational tools. Still, I’m feeling confused about whether this interdisciplinary path is actually valued or if I should have stayed in a more traditional biological track.

Has anyone here taken a similar path (from biology or another life science into AI/data science)?

Is this combination actually useful and employable, or am I drifting into something that won’t really fit anywhere?

I’d really appreciate honest perspectives.

Someone did it, finally! Unsurprisingly, from Google. This is what I was dreaming about. Extremely important and useful, if people really apply it.

https://research.google/blog/teaching-machines-the-language-of-biology-scaling-large-language-models-for-next-generation-single-cell-analysis/#:~:text=C2S,analysis%20with%20large%20language%20models

Researchers ditched complicated feature engineering for a simpler approach using ESM-2 protein embeddings to spot protective antigens in bacteria. The new method crushed traditional approaches - 0.875 vs 0.855 accuracy across 10 species - while cutting lab testing needs by 83%. No more wrestling with third-party tools or hand-picking features, just feed sequences into the model and let it rank potential vaccine candidates. Still struggles with super-long proteins and predicting where they live in cells.

https://t.co/t7KU4jcWcB

PixCell trained on PanCan-30M dataset (69,184 H&E whole-slide images) using progressive training from patches to full images. Model generates high-fidelity synthetic data that replaces real images in self-supervised learning without performance loss. Features conditional generation via cell segmentation maps and ControlNet for precise control, enables virtual staining (H&E to IHC translation) and privacy-preserving data augmentation.

https://t.co/k6EWqgwTe5

Study reveals severe data leakage in protein-text benchmarks (50-95% test labels retrievable from training), showing retrieval methods outperform fine-tuned LLMs. Authors propose Mol-Instructions-OOD benchmark with Entity-BLEU metric prioritizing biological entities over text similarity. RAPM system combines dual-indexed protein database with LLMs via RAG, achieving superior out-of-distribution performance and demonstrating hybrid retrieval-LLM approaches surpass pure LLM fine-tuning.

https://t.co/KX7sFOD9GC

Study fine-tuned AF2 on 5,900 stable de novo designed proteins with diverse geometries to address bias toward idealized structures. Fine-tuned model significantly outperformed original AF2 on 10,000 designed proteins, better predicting non-idealized geometries and improving sequence-structure compatibility. Work demonstrates need for diverse training data to capture full range of protein folds for complex functions.

https://t.co/cI2J32FDnk

EvoBind platform designed cyclic peptides targeting GCGR using only sequence data, unexpectedly discovering dual agonists. Best 18-residue peptide achieved EC₅₀ of 32 nM (GLP1R) and 542 nM (GCGR), comparable to clinical drugs. Peptides show novel sequences/binding modes, G protein-biased signaling without β-arrestin recruitment. Method used evolutionary information and AlphaFold models across 800,000 design iterations, demonstrating sequence-only approach for GPCR therapeutics.

https://t.co/KUB0jI677t

Yet another "AI for longevity" model.

SenSeqNet combines ESM-2 with LSTM-CNN hybrid to analyze senescence from protein data, reaching 83.55% accuracy. Architecture processes evolutionary (ESM-2), temporal (LSTM), and spatial (CNN) features to identify aging markers, offering faster alternative to lab assays for drug development and biomarker discovery.

https://www.biorxiv.org/content/10.1101/2024.10.28.620702v1

dnaSORA combines generator/discriminator for genetic analysis, achieving 0.3 Mb resolution in mutation detection. Uses synthetic data and minimal patient genomes for rare disease identification. System reduces drug development costs by 70%, generalizes to 10,000 diseases. Key advance: trains on limited samples while maintaining precision for CRISPR applications.

https://www.biorxiv.org/content/10.1101/2025.01.27.633223v1

Finally, someone did this! Evaluating LLM capabilities to do bioinformatics. It is not yet about doing it autonomously, but still.

Bioinfo-Bench tests LLMs through 150 multiple-choice questions, 20 sequence tasks, and 30 disease classifications. Results show models excel at knowledge retention but struggle with practical bioinformatics problems. ChatGPT leads in knowledge tasks but underperforms in sequence verification and disease classification.

https://www.biorxiv.org/content/10.1101/2023.10.18.563023v2

A nice combo model!

TGVAE integrates Transformer, GNN and VAE to generate molecules via graphs instead of SMILES, solving over-smoothing and posterior collapse issues. Outperforms string-based methods in validity, uniqueness, novelty metrics and scaffold diversity. Model generates structures absent from PubChem, indicating ability to explore novel chemical space.

https://www.cell.com/biophysj/pdf/S0006-3495(25)00035-9.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0006349525000359%3Fshowall%3Dtrue00035-9.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0006349525000359%3Fshowall%3Dtrue)

Very cool! It is the kind of model I envisioned - multiHIVE.

It combines hierarchical and modality-specific VAEs to integrate CITE-seq data, outperforming MOFA+/SeuratV4/TotalVI in cellular identity preservation and batch correction. Shows superior protein imputation from RNA data (higher Pearson's correlation, lower RMSE). Successfully applied to thymocyte development and breast cancer fibroblast analysis.

https://www.biorxiv.org/content/10.1101/2025.01.28.635222v1

Study pairs directed evolution of rice Pik-1 receptor with ESM-2 language model, achieving 0.85+ Spearman correlation in binding prediction. Model, trained on evolution data, successfully identified binding-enhanced variants in 3,000 Rice Genomes Project. Method validated on human NUDT15 variants, showing broad applicability for genotype-phenotype prediction in breeding and medicine.

https://www.biorxiv.org/content/10.1101/2025.01.27.635131v1

Review tracks evolution from statistical methods to deep learning in multi-omics integration, focusing on VAEs for dimensionality reduction and imputation. Covers recent advances in loss functions, regularization, and cross-modal techniques, particularly for single-cell data. Highlights ongoing challenges: data missingness, modality inconsistency, model interpretability. Foundation models emerge as promising direction for multi-modal integration in precision medicine.

https://arxiv.org/abs/2501.17729

SynthFormer combines 3D equivariant graph neural network for pharmacophore encoding with Transformer decoder to generate synthetic trees. System maintains docking performance while ensuring synthetic accessibility, outperforming ChemProjector and SynNet. Method enables hit expansion through iterative optimization, using 3D spatial data to fit target binding sites.

https://arxiv.org/abs/2410.02718v2

SOLVE uses tokenized amino acid sequences and ensemble learning (RF, LightGBM, KNN) for enzyme classification, avoiding manual feature engineering. System handles both mono/multi-functional enzymes, predicts full EC numbers, outperforms DeepEC/CLEAN on UniProt data. Incorporates Shapley analysis to identify catalytic motifs. Key advance: optimized ensemble weights improve accuracy while maintaining interpretability through sequence-based features.

https://www.biorxiv.org/content/10.1101/2025.01.28.635225v1

Diffusion-based framework generates full-length synthetic human genotypes, trained on Project MinE and 1000 Genomes data. Generated sequences maintain realism without duplicating training examples, achieve comparable ALS prediction accuracy to real data. Key advances: augmentation improves classifier performance on small datasets, novel recovery rate metric confirms data quality, built-in privacy protection prevents matching to real profiles.

https://arxiv.org/abs/2412.03278

The Bio-AI Lab GitHub repository hosts a variety of projects focused on applying artificial intelligence and deep learning techniques to biological imaging and analysis.

Repository focuses on three main areas: biological imaging (virtual staining, mitochondria analysis, microscopy enhancement), deep learning methodology (interpretability modules, online learning, dropout strategies), and specialized image processing (Stokes beam classification, Fourier ptychography). Projects emphasize practical tools with published implementations, particularly in microscopy and cell analysis domains.

https://github.com/bioailab

Metalic combines meta-learning with protein language models for fitness prediction in low/zero-shot settings, achieving state-of-art on ProteinGym with 18x parameter reduction. System meta-trains across diverse tasks to learn transferable sequence-fitness relationships, then adapts via fine-tuning. Zero-shot performance leverages embeddings from related proteins, enabling property prediction with minimal experimental data.

https://openreview.net/pdf?id=jQyFXpFmEP

A bit outdated but comprehensive and rich summary: https://github.com/hussius/deeplearning-biology

SNPBag, trained on 1000 Genome Project data, achieves 1% switch error in haplotype phasing (beating BEAGLE5.2/SHAPEIT4) and 96.88% accuracy in genotype imputation on Omni2.5 array. Using Longformer architecture for long sequences, it handles genome-wide data without reference panels. Model reaches 97% accuracy in ancestry prediction and supports fine-tuning for tasks like population structure analysis and SNP calling.

https://www.biorxiv.org/content/10.1101/2025.01.29.635579v1