matrix factorization

Hybrid Clustering of single-cell gene-expression and cell spatial information via integrated NMF and k-means

This paper introduces scHybridNMF, a method that combines gene expression and spatial location data to improve cell type clustering in single-cell data.

Fast Alignment-Free Similarity Estimation By Tensor Sketching

The paper introduces Tensor Sketch, a method for fast and accurate edit distance estimation in biological sequences, outperforming existing methods in speed and accuracy.

abPOA: an SIMD-based C library for fast partialorder alignment using adaptive band

abPOA is a fast partial order alignment library using SIMD acceleration, achieving 15x speed improvement over SPOA with similar accuracy.

Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs

This paper estimates indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half-sibling pairs, demonstrating the feasibility of this approach with GWAS data.

A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank

This paper introduces a scalable framework for sparse regression on large-scale data, demonstrating improved prediction performance on UK Biobank traits using the BASIL method and snpnet package.

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

The paper integrates anatomy ontology data with PPI networks to improve candidate gene prediction accuracy for anatomical entities in zebrafish and mouse.

Genome-wide association study of suicide death and polygenic prediction of clinical antecedents

This paper conducts a genome-wide association study of suicide death and shows polygenic scores predict suicide case-control status and related psychiatric traits.

A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes

This paper introduces SampleLASSO, a method for imputing unmeasured gene expression in microarray data, showing it outperforms existing methods across multiple tasks.

Mosaic trisomy of chromosome 1q in human brain tissue associates with unilateral polymicrogyria, very early-onset focal epilepsy, and severe developmental delay

The paper identifies a chromosomal duplication of chromosome 1q in a subset of polymicrogyria patients, associated with specific clinical features and distinct DNA methylation patterns.

A framework to efficiently smooth L1 penalties for linear regression

This paper introduces a smoothing framework for L1 penalized regression that provides accurate predictions with theoretical guarantees and comparable runtime to existing methods.

Elucidating the Joint Genetic Architecture of Mood Disorder and Schizophrenia

This study identifies 308 genetic loci associated with mood disorder and schizophrenia across East Asian and European populations, revealing shared and distinct genetic mechanisms.

The changing mouse embryo transcriptome at whole tissue and single-cell resolution

The paper quantifies the mouse embryo transcriptome from E10.5 to birth using whole-tissue and single-cell RNA-seq, identifying key gene expression patterns and cell types.

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

The paper introduces coupled tensor-tensor decomposition for EEG-fMRI data fusion to extract task-related features, showing high correlation with memory task components.

SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

SPRDA predicts disease-associated piRNAs using a matrix completion approach based on structural perturbation, achieving high performance on benchmark datasets.

Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

The paper identifies mitochondrial gene networks associated with feed efficiency in pigs through transcriptomic analysis and pathway enrichment.

Learning a latent representation of human genomics using Avocado

This paper introduces Avocado, a deep learning method that learns latent representations of human genomics data, enabling accurate imputation of unperformed experiments and improved performance on genomics tasks.

Connectivity dynamics from wakefulness to sleep

This study analyzes connectivity dynamics in fMRI data across wakefulness and sleep stages using ICA and sliding window techniques, showing improved classification of sleep states and motion effects with shorter windows.

Iterative Hard Thresholding in GWAS: Generalized Linear Models, Prior Weights, and Double Sparsity

This paper introduces an improved iterative hard thresholding method for GWAS that enhances true positive rates and reduces false positives compared to traditional approaches.

Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures

The paper identifies gene-specific quantification biases in RNA-seq software by analyzing datasets with multiple computational pipelines and matrix factorization.

Identification of conserved proteomic networks in neurodegenerative dementia

The paper identifies conserved proteomic changes in neurodegenerative dementias, distinguishing AD and PSP genetic risk modules and showing some variance in protein expression is post-transcriptional.

Laboratory evolution of multiple E. coli strains reveals unifying principles of adaptation but diversity in driving genotypes

This study uses multi-omics data from evolving E. coli strains to identify convergent and divergent adaptation principles at metabolic, transcriptomic, and genetic levels.

Multi-parametric quantitative spinal cord MRI with unified signal readout and image denoising

This paper introduces a unified signal readout approach for multi-parametric spinal cord MRI, combined with MP-PCA denoising, improving the quality and consistency of microstructural metrics.

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

This study integrates multiple scATAC-seq datasets using matrix factorization and transfer learning to identify common regulatory patterns across datasets.

Inspecting abundantly expressed genes in male strobili in sugi (Cryptomeria japonica D. Don) via a highly accurate cDNA assembly

The paper assembles a high-quality transcriptome from male strobili of sugi, identifies expressed genes, and demonstrates its utility for functional and evolutionary studies.

The potential genetic network of human brain SARS-CoV-2 infection

The paper identifies gene clusters in the human brain associated with ACE2 expression and links them to organs affected by SARS-CoV-2 infection.

Two synthetic 18-way outcrossed populations of diploid budding yeast with utility for complex trait dissection

The paper describes two synthetic outcrossed yeast populations for complex trait analysis, showing high recombination and accurate haplotype frequency estimation from pooled sequencing data.

Multi-scale deep tensor factorization learns a latent representation of the human epigenome

The paper introduces Avocado, a deep tensor factorization method that learns a latent representation of the human epigenome, improving imputation and performance on genomics tasks.

Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

The paper improves causal gene identification and transcriptome prediction by integrating fine-mapping and QTL tissue-sharing information into transcriptomic imputation models.

A tissue- and organ-based cell biological atlas of obesity-related human genes and cellular pathways

The paper constructs a tissue- and organ-based subcellular-function atlas of obesity-related genes and pathways using publicly available datasets and integration methods.

Approximate k-nearest neighbors graph for single-cell Hi-C dimensional reduction with MinHash

The paper introduces an approximate k-nearest neighbors method using MinHash to reduce the dimensionality of single-cell Hi-C data, enabling efficient clustering of large datasets.