Michal Linial B

This study identifies miR-99b's nuclear role in regulating SPACA6-AS1 pre-mRNA splicing and its contribution to breast cancer progression.

OTRec is a deep learning model that recommends druggable disease-target associations, outperforming existing methods in both retrospective and cross-validation settings.

This study shows DJ-1/PARK7 influences miRNA network plasticity during genotoxic stress by altering miRNA abundance and regulatory machinery.

This study identifies micropeptides encoded by lncRNAs in cancer, showing they have distinct sequence features and structural potential, suggesting they may serve as biomarkers and therapeutic targets.

The paper constructs a comparative regulatory atlas of gonad development using mouse and human data, identifies a conserved enhancer critical for gonadal development, and prioritizes non-coding variants in DSD patients.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

MAGI provides mechanistic insights into genetic variants by integrating genomic data and clinical annotations to improve variant interpretation and generate testable hypotheses.

This study shows that altering DJ-1 levels changes non-coding RNA expression and cellular homeostasis after X-ray exposure.

The paper identifies high-confidence breast cancer predisposition genes using multi-ethnic genomic data and integrates GWAS, TWAS, and PWAS to refine the genetic architecture of heritable breast cancer.

The paper investigates the molecular mechanism of the Gαo E246K mutation in neurodevelopmental disorders, showing it disrupts the GTPase cycle by impairing Gβγ dissociation, leading to dominant-negative effects.

This study identifies miRNA expression changes in activated microglia, showing their role in inflammation and potential as biomarkers for neurodegenerative diseases.

This study identifies transitional lncRNAs (Tr-lncRNAs) associated with cancer progression stages and metastasis using TCGA data, revealing cancer-type specific and shared lncRNA signatures.

This study identifies xCT regulation pathways in colon cancer using patient-specific models and reveals potential therapeutic targets for chemotherapy resistance.

PWAS Hub identifies gene-disease associations for 99 common diseases using proteome-wide association analysis, with sex-specific insights and comparison to GWAS results.

This study shows that knocking down DJ-1 in HEK293 cells activates an antiviral innate immune response and affects RNA metabolism and mitochondrial functions.

The paper analyzes RNA-seq data from CRC patients to identify SLC7A11's role in metabolic regulation and cell proliferation, linking it to clinical outcomes and therapeutic responses.

DROP-DEEP improves polygenic risk score prediction by using deep learning for dimensionality reduction, outperforming traditional methods on hypertension and other traits.

The paper uses protein language models to distinguish human and viral proteins, identifying immune escape mechanisms through error analysis.

This paper uses deep learning to detect anomalous proteins by identifying unusual properties in function, phylogenetic families, and sequence segmentation.

This study examines how microglia respond to LPS and bzATP activation, finding that only combined treatment leads to a sustained inflammatory gene expression profile.

This paper shows that GSD1a patient fibroblasts retain a reversible disease phenotype in vitro, driven by epigenetic and transcriptional changes, and that a drug can reverse these effects.

This paper evaluates covariate selection in GWAS for 36 UK-Biobank traits, proposing guidelines to improve power and reduce computational burden.

The paper uses proteome-wide association study (PWAS) and other methods to identify genes associated with hypothyroidism, revealing different facets of its etiology including autoimmunity and sex-related gene enrichment.

The paper predicts miRNA-regulated genes using a protein-based machine learning framework, showing better performance than genomic approaches and identifying exceptions like GPCRs.

This study uses proteome-wide association study (PWAS) to identify genes associated with hypertension, finding sex-dependent genetic effects and recessive inheritance.

A ~16M-parameter protein language model, pretrained on ~106M UniRef90 proteins by denoising sequences and Gene Ontology annotations, rivals larger TAPE models using linear-cost global attention.

This study uses GWAS and PWAS on UK Biobank data to identify 137 cancer risk loci, highlighting recessive effects and cancer-type specificity.

The paper constructs human-specific amino-acid substitution matrices from genetic variation data to better represent proteomic variation within the human population and reveal new evolutionary insights.

The paper identifies a de novo STXBP1 mutation in a child with developmental delay and spasticity, showing how it disrupts the STXBP1-syntaxin 1A interaction through structural analysis.

PWAS detects protein-coding genes associated with phenotypes by analyzing proteomic context and using machine learning, showing improved detection of causal genes and new associations.