Fengzhu Sun

Benchmarking Alignment Strategies for Hi-C Reads in Metagenomic Hi-C Data

The paper benchmarks alignment strategies for Hi-C reads in metagenomic data, finding BWA MEM -5SP performs best in inter-contig read pairs and binning quality.

GBoost-CTL: A novel method in multi-tissue transcriptome-wide associations studies in cross-tissue learner incorporating GWAS information

GBoost-CTL improves multi-tissue transcriptome-wide association studies by integrating GWAS data and weighting tissues based on predictive accuracy and GWAS signal.

TWAS-CTL: A robust and efficient method for multi-tissue transcriptome-wide association studies using cross-tissue learners

TWAS-CTL improves multi-tissue TWAS by combining single-tissue learners, achieving higher power and efficiency than existing methods.

DeepMicroClass sorts metagenomes into prokaryotes, eukaryotes and viruses, with marine applications

DeepMicroClass classifies metagenomic contigs into five sequence classes with high accuracy, outperforming existing methods in marine metagenomic analysis.

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

This study compares normalization methods for metagenomic data to improve phenotype prediction across studies, finding that batch correction methods perform best while population and disease effects limit normalization effectiveness.

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

This paper evaluates methods for integrating heterogeneous genomic studies to improve phenotype prediction, finding that batch normalization with merging or integration strategies enhances classifier performance.

MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

MetaBinner improves metagenomic binning by integrating multiple features and SCG information, outperforming existing methods on simulated and real datasets.

Critical Assessment of Metagenome Interpretation - the second round of challenges

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

HiCBin: Binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

HiCBin improves metagenomic binning using Hi-C contact maps, achieving better accuracy than existing methods in recovering high-quality MAGs.

HiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inflated negative binomial regression

HiCzin normalizes metagenomic Hi-C data and detects spurious contacts using zero-inflated negative binomial regression, improving bias correction and contig clustering.

A global overview of genetically interpretable comorbidities among common diseases in UK Biobank

The paper identifies 11,285 genetically interpretable comorbidities among 439 diseases using UK Biobank data, showing shared genetic components and hub diseases involved in comorbidity networks.

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

This study improves brain age prediction using deep learning and identifies four genetic loci associated with brain aging, three of which are novel.

Assessing Overall Reproducibility for Large-scale High-throughput MRI-based Association Studies

This study introduces an overall reproducibility index for MRI-based association studies and evaluates its performance across multiple datasets, showing that sample size and experimental factors influence reproducibility.

CRAFT: Compact genome Representation towards largescale Alignment-Free daTabase

CRAFT learns compact sequence representations for fast alignment-free comparison of genomic and metagenomic data, achieving efficient storage and query performance.

A network-based integrated framework for predicting virus-host interactions

The paper introduces a network-based framework to predict virus-host interactions, achieving higher accuracy than previous methods and expanding known interactions across diverse datasets.

Metagenomic unmapped reads provide important insights into human microbiota and disease associations

The paper introduces MicroPro, a metagenomic analysis tool that improves disease association predictions by including reads from unknown organisms, and identifies new microbial and viral associations with colorectal cancer, type-2 diabetes, and liver cirrhosis.

Benchmarking of alignment-free sequence comparison methods

This paper benchmarks alignment-free sequence comparison methods across five research applications and provides a web resource for evaluating and comparing these methods.

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

NetGO improves large-scale protein function prediction by integrating sequence and network information, outperforming existing methods on CAFA benchmark data.

A new statistic for efficient detection of repetitive sequences

The paper introduces a new statistic for efficiently detecting repetitive sequences in genomic data and demonstrates its effectiveness across multiple scenarios.

Association of brain age with smoking, alcohol consumption, and genetic variants

This study investigates the association between brain age and smoking, alcohol consumption, and genetic variants using UK Biobank data, finding significant links with smoking and alcohol use but not genetic variants.

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

GOLabeler improves sequence-based protein function prediction by integrating multiple classifiers in a learning-to-rank framework, showing better performance than existing methods.