Yang‐Yu Liu

The paper constructs a urinary microbiome catalog and identifies antimicrobial peptides from it that inhibit uropathogenic E. coli.

The study identifies oral microbial signatures associated with IBD and PSC and their potential translocation to the gut, supported by external validation.

This paper develops a machine learning framework to provide personalized dietary recommendations for achieving desired gut microbial compositions using population-level cohort data.

This paper introduces VIP2B, a method to extract viral features from bulk metagenomes, showing that viral data improves disease diagnosis and reveals distinct gut virome community states.

This study investigates how probiotic Bifidobacterium animalis subsp. lactis V9 competes with native gut bacteria, finding that a high-fiber diet enhances its colonization and competition with Parabacteroides distasonis.

The paper quantifies variation in gut microbial metabolism of dietary compounds and identifies compounds specific to few species, proposing a method for synbiotic design.

The paper identifies a two-layered regulatory network governing temporal patterning in neocortical development through single-cell multi-omics analysis.

The paper introduces MultiCAST, a high-throughput CRISPR-associated transposon system for bacterial gene disruption, enabling rapid and scalable genetic screens using amplicon sequencing.

This paper develops an optimization-based dietary recommendation approach to improve diet scores using simulated annealing and evaluates it on Diet-Microbiome Association study data.

PHILM, a deep learning framework, predicts phage-host interactions from metagenomic data with higher accuracy than traditional methods, identifying more interactions and capturing microbial succession patterns.

This study uses computational methods and metagenomic data to identify microbial strains that can inhibit CDI, supporting the design of live biotherapeutic products.

The paper investigates collective motion patterns in bird flocks and develops a model based on motion salience to explain hierarchy formation and swarm behavior.

This study introduces METRIC, a deep-learning method that uses gut microbiome data to correct random errors in self-reported dietary assessments, showing effectiveness in simulated and real-world datasets.

This paper introduces a computational method to identify causal gut microbes responsible for colonization resistance against pathogens, validated using mouse infection data.

The paper identifies nutritional redundancy in dietary intake and shows its association with lower risks of cardiovascular disease and type 2 diabetes after adjusting for age.

The paper introduces MAP2B, a metagenomic profiler that reduces false positives and improves abundance estimation using type IIB restriction sites, outperforming existing methods on benchmark datasets.

This paper introduces a statistical physics approach using the random-field Ising model to detect disease modules, showing improved efficiency and robustness compared to existing methods.

StrainPanDA links strain composition and gene content in metagenomic data to understand microbial functions and phenotypes.

The paper introduces CHESHIRE, a deep learning method to predict missing reactions in metabolic networks using topology, outperforming existing methods and improving phenotypic predictions.

The paper introduces a deep learning framework to predict microbial community compositions from species assemblages using synthetic and experimental data.

This study investigates higher-order relationships between brain microstructure and functional connectome, finding they correlate with personality, age, and gender differences across brain regions.

The paper evaluates 24 network-based methods for predicting protein-protein interactions across five interactomes, identifies top-performing methods, and validates their predictions experimentally.

This study quantifies protein-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics and network analysis, revealing high redundancy and environmental influences on functional organization.

This paper highlights challenges in benchmarking metagenomic profilers due to confusion between sequence abundance and taxonomic abundance.

This paper introduces a deep generative model for link prediction that works across different network types and domains, showing superior performance compared to existing methods.

This study identifies peripheral blood microbial signatures associated with COPD and exacerbation phenotypes in smokers.

The paper develops a method to identify sensor species in ecosystems to predict critical transitions using network structure, validated on experimental lake data.

This paper develops a network-based framework to control microbial communities by identifying driver species, validated on gut and sponge microbiota.

The paper identifies NFATC4 as a key regulator of Type 2 Diabetes genes by analyzing a gene regulatory network in pancreatic islets and linking it to eQTLs and differential expression.