Kasper Lage

The paper introduces DeepCompARE, an in silico ablation framework to analyze TF cooperativity, finding most TF motifs are independent with defined synergy and redundancy effects.

The paper identifies three neuron-specific schizophrenia protein networks enriched for genetic and transcriptional perturbations and validates their disruption in 22q11.2 deletion carriers.

The paper uses hiPS cell-derived cortical organoids to study how genetic mutations in ASD converge on shared transcriptional changes during development.

The paper investigates the role of CSMD1 in opposing the complement cascade in neural tissues and links its dysfunction to schizophrenia risk.

The paper introduces a method to prioritize causal genes for complex traits by integrating GWAS data with gene expression and biological data, achieving high precision in gene-trait pair identification.

The paper identifies a protein-protein interaction network in human excitatory neurons for 13 ASD risk genes, revealing novel interactions and convergent mechanisms contributing to autism spectrum disorders.

The paper introduces Fit4Function, a machine learning approach to engineer AAV capsids with multiple traits, achieving high transduction efficiency and cross-species prediction.

Genoppi integrates proteomic and genetic data to identify cell-type-specific interactions and their role in disease.

This paper introduces a systematic auditing framework to identify biases in machine learning applications in biology, showing that biases can hinder model generalizability and lead to poor performance when training data lacks sufficient signal.

This study uses human brain organoids to show that ASD risk genes cause accelerated development of specific cortical neuron classes.

The paper analyzes missense variants in 1,330 disease genes using 3D protein structures to identify mutational hotspots and their functional impact.

The paper identifies brain-wide and region-specific gene co-expression modules using RNA-seq data and links them to disease risk variants and biological processes.

The paper uses DeGAs to identify genetic components across 2,138 phenotypes in the UK Biobank, revealing novel adipocyte biology through variant and gene contributions to obesity-related traits.

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

The paper integrates genetic, genomic, and functional data to identify convergent biological pathways in type 2 diabetes, revealing enriched functional connectivity among causal genes.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

GeNets provides a platform to compare genomic networks using machine learning and allows analysis of genetic and RNA sequencing data.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.

The paper identifies 685 putative cancer-related genes by analyzing mutation depletion in protein interaction networks across 4,700 cancer genomes.

Genoppi integrates proteomics and genetic datasets to identify biological relationships, offering a user-friendly web application for this purpose.

The paper constructs a scored human protein-protein interaction network, InWeb_IM, which has more interactions and better functional signal than existing resources, and applies it to annotate genes from cancer and neuropsychiatric disease genomes.

The paper quantifies network mutation burdens across 21 tumor types to identify putative cancer genes, finding 62 new candidates with greater sensitivity for low-frequency mutations.