How AlphaGenome Tackles Variant Effect Prediction
AlphaGenome processes 1 million DNA base pairs to predict variant effects across 7,000+ genomic tracks in one second, outperforming specialized models on 25 of 26 VEP benchmarks.
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#deep learning
7 articles tagged with #deep learning.
How AlphaGenome Models Gene Regulation: 2D Embeddings, Splicing, and the Race to Read Non-Coding DNA
A technical look at AlphaGenome's architecture, its 2D pairwise embeddings for splicing prediction, and what the model means for clinical variant interpretation.
A Bioinformatician's Guide to Choosing Genomic Foundation Models
A practical guide to selecting genomic foundation models for bioinformatics tasks. Covers ESM-2, DNABERT-2, HyenaDNA, Nucleotide Transformer, scGPT, and Evo with specific recommendations for DNA sequence analysis, protein structure prediction, and single-cell analysis based on hardware requirements, inference speed, and task type.
Engram: How DeepSeek Added a Second Brain to Their LLM
A technical deep dive into DeepSeek's Engram architecture, which introduces conditional memory as a new axis of sparsity for large language models.
VL-JEPA: Why Predicting Embeddings Beats Generating Tokens for Vision-Language AI
VL-JEPA achieves 50% parameter reduction and 2.85x faster decoding by predicting embeddings instead of generating tokens, offering a compelling alternative to autoregressive vision-language models.
Why 1000-Layer Networks Finally Work for Reinforcement Learning
Recent research shows 1024-layer networks achieve 2x to 50x improvements in goal-conditioned RL. Here's why extreme depth works now, and when you should consider it for your own agents.
Tensor Logic: One Equation to Rule Them All
Pedro Domingos proposes that neural networks and symbolic AI are the same mathematical operation - a logical rule can be equivalently written as a tensor equation in Einstein summation notation. If true, we've been building separate tools for problems that share identical structure.
