David H. Haussler

PULSE, a foundation model trained on neural recordings, reveals how disease variants affect network development and how circuits encode information in brain organoids.

The paper introduces MYCEL, a graph neural network that improves decoding of brain anatomy from neuronal neighborhoods by learning local pooling, outperforming fixed aggregation on multiple datasets.

HIPPIE is a deep learning model that classifies neurons from electrophysiological recordings by combining self-supervised pretraining and supervised fine-tuning, showing better performance than unsupervised methods.

The paper presents a cloud-based pipeline for analyzing and visualizing neuronal electrophysiology data from HD-MEAs, demonstrating its utility in simplifying data analysis and understanding neural activity.

This paper develops a cloud-connected stem cell platform to enhance scientific identity in underrepresented students, showing improved outcomes in competence and research readiness.

This paper develops a 3D-printed, servo-actuated microvalve system for automated organoid culture, validated in mouse and human models with comparable viability and gene expression to manual methods.

SpikeLab is a text-to-analysis framework for neural spike data that ensures correct and reproducible results using agentic systems, outperforming other models in a controlled benchmark.

The paper analyzes chromosome 22q11.2 haplotypes to show how structural variations in specific regions correlate with microdeletion and inversion risks across populations.

This study develops a microfluidic platform to induce spatially segregated ventral and dorsal domains in mouse forebrain organoids without fusion, enabling study of tissue regionalization.

This study uses mouse forebrain organoids to show how variations in cellular composition influence the self-organization of neural circuits, revealing intrinsic principles of forebrain circuit assembly.

This paper introduces an automated in-incubator microscope for long-term live cell imaging, enabling high-frequency, continuous data collection with improved thermal stability and operational reliability.

This study demonstrates goal-directed learning in cortical organoids using a closed-loop electrophysiology framework and artificial reinforcement learning.

CytoVerse enables browser-based single-cell analysis using Foundation Models by deploying models in ONNX, compressed indexing, and secure embedding sharing.

The paper uses long-read sequencing to identify pathogenic variants in autism cases, finding three pathogenic mutations and nine candidate structural variants missed by short-read sequencing.

This paper introduces vascularized cortical assembloids (vCAs) that model neurovascular co-development and show improved cellular specialization and connectivity compared to standard cortical organoids.

This paper introduces a sealed recirculatory system for organoid culture that eliminates the need for CO2 incubators and enables continuous live imaging and metabolic viability.

This paper introduces an automated brain organoid platform with IoT integration for continuous monitoring and control, showing increased neural activity and stable firing rates during automated media exchange.

The paper evaluates the CARE pipeline for identifying druggable targets in pediatric cancers using RNA-Seq data from 33 patients, showing 94% of cases had clinically significant findings.

RT-Sort detects and sorts action potentials in real time with millisecond latencies using propagation patterns and a convolutional neural network.

The paper uses high-density electrophysiology to study epileptiform activity in human hippocampal slices from epilepsy patients, revealing unexpected network oscillations in dentate gyrus slices.

The paper identifies a high-frequency, millisecond-scale brain state embedding that persists across sleep and wake states and correlates with behavior.

SIMS is a deep learning tool for single-cell RNA sequencing classification that outperforms existing methods in accuracy and versatility across complex tissues and developmental stages.

The paper introduces a new transfer function called Refractory SoftPlus to improve mean-field models of neuronal networks, enabling accurate predictions of network responses and bifurcation analysis without restrictive assumptions.

The paper introduces Minigraph-Cactus, a pangenome pipeline that constructs pangenome graphs directly from whole-genome alignments, demonstrating improved performance in mapping and variant calling across human and Drosophila data.

The paper shows recombination between heterologous human acrocentric chromosomes using pangenome data, revealing pseudo-homologous regions linked to Robertsonian translocations.

The paper identifies cis-acting elements linking alternative splicing to translational control across primate species and shows sequence variation affects polyribosome association.

The paper evaluates sequencing and assembly methods to create high-quality diploid human genome assemblies, finding that long reads and haplotype sorting improve accuracy and completeness.

The paper introduces a pedigree-aware workflow using pangenome graphs to improve variant calling in rare disease analysis, detecting more deleterious variants in probands than unaffected siblings.

The paper introduces Giraffe, a pangenome mapper that improves genotyping of SNVs, indels, and SVs in 5,202 human genomes using the vg toolkit.

The paper introduces Piphys, an open-source platform for neural recording that enables remote control and scalability through cloud-based software and low-cost hardware.