matrix factorization

Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques

This paper accelerates multi-electrode array simulations using sparse and low-rank matrix techniques, achieving up to 133x speedup with minimal error.

A single low-dimensional neural component of motor unit activity explains force generation across repetitive isometric tasks

The paper uses PCA and FA to show a single low-dimensional motor unit component explains most force oscillations during repetitive isometric tasks.

A multi-omics and cell type-specific characterization of the ventral striatum in human cocaine use disorder

This study characterizes molecular changes in the ventral striatum of individuals with cocaine use disorder using multi-omics and single-cell approaches, identifying perturbations in metabolic pathways and cell-type-specific alterations.

FAMILY-BASED RNA SEQUENCING IN BIPOLAR DISORDER FOR CANDIDATE GENE AND PATHWAY IDENTIFICATION

This study uses RNA sequencing in multiplex bipolar disorder families to identify differentially expressed genes and pathways, finding significant lncRNAs and ion homeostasis dysregulation.

DNA methylation clocks struggle to distinguish inflammaging from healthy aging, but feature rectification improves coherence and enhances detection of inflammaging

Lifting the curse from high dimensional data: Automated projection pursuit clustering for the variety of biological data modalities

The paper introduces APP, a clustering method that reduces high-dimensional biological data to lower dimensions to improve clustering performance across multiple data types.

Hypothesis Generation For Rare and Undiagnosed Diseases Through Clustering and Classifying Time-Versioned Biological Ontologies

This paper identifies potential gene-to-phenotype connections for undiagnosed diseases by clustering time-versioned biological ontologies and analyzing network data.

GP-ML-DC: An Ensemble Machine Learning-Based Genomic Prediction Approach with Automated Two-Phase Dimensionality Reduction via Divide-and-Conquer Techniques

GP-ML-DC improves genome prediction by combining machine learning and deep learning with a two-phase dimensionality reduction approach, outperforming existing methods on cattle milk traits.

Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex

The paper introduces an algorithm for non-cleavable crosslink identification in MS Annika, revealing the structure of the C. elegans Box C/D complex and enabling proteome-wide analysis.

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction

This study introduces a novel AI-based method using tensor decomposition to identify drug-gene-disease interactions from protein-protein interactions without prior disease or drug information.

A Cost-effective, High-throughput, Highly Accurate Genotyping Method for Outbred Populations

The paper describes a cost-effective, high-throughput genotyping method for heterogeneous stock rats using low-coverage sequencing data, achieving high accuracy comparable to high-coverage sequencing.

ARID1A-induced transcriptional reprogramming rewires signalling responses to drug treatment in melanoma

This study reveals how ARID1A-mediated transcriptional rewiring drives resistance to MAPK inhibitors in melanoma by altering signalling pathways, immune interactions, and receptor dynamics, highlighting potential targets for combinatorial therapies.

Multimodal neural correlates of childhood psychopathology

The paper identifies multimodal brain network patterns associated with dimensions of psychopathology using the Adolescent Brain Cognitive Development dataset.

iSIM-sigma: efficient standard deviation calculation for molecular similarity

The paper introduces efficient methods to calculate standard deviation of molecular similarities with reduced computational complexity.

Conserved moiety fluxomics

This paper introduces a new method for genome-scale metabolic flux inference using conserved moieties, demonstrating its computational efficiency and accuracy with isotopic labeling data.

A Comprehensive Human Embryogenesis Reference Tool using Single-Cell RNA-Sequencing Data

The paper develops a comprehensive reference tool for human embryogenesis using integrated single-cell RNA-sequencing data to benchmark embryo models.

Sparse matrix factorization robust to sample sharing across GWAS reveals interpretable genetic components

This paper introduces GLEANR, a method for sparse matrix factorization of GWAS summary statistics that accounts for sample sharing and identifies interpretable genetic factors.

ADM: Adaptive Graph Diffusion for Meta-Dimension Reduction

ADM combines multiple dimension reduction techniques using graph diffusion to better capture complex data structures and sample heterogeneity.

Low-Rank Full Matrix Factorization for dropout imputation in single cell RNA-seq and benchmarking with imputation algorithms for downstream applications

This paper develops an imputation algorithm for single-cell RNA-seq and benchmarks it against other methods across various downstream analyses.

memerna: Sparse RNA Folding Including Coaxial Stacking

The paper introduces memerna, a fast RNA folding algorithm that incorporates coaxial stacking and sparsity, achieving the fastest exact RNA folding implementation among popular packages supporting this feature.

PARMIK: PArtial Read Matching with Inexpensive K-mers

PARMIK improves metagenomic sequence search by using inexpensive k-mers to achieve higher recall and faster alignment than BLAST.

Integrated Isoform-Resolved Transcriptomic Analysis of Gossypium barbadense and Gossypium hirsutum Fibers

This study uses long-read and short-read RNA sequencing to analyze isoform expression in two cotton species, identifying stage-specific alternative splicing and co-expression networks.

Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization

This paper applies non-negative matrix factorization to reduce and classify mass spectrometry imaging data in a visceral pain model, achieving high accuracy in distinguishing pain conditions.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

The paper identifies cell-type specific lung cancer susceptibility genes by integrating single-cell multi-omics data with GWAS loci.

Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

This paper introduces an algorithm to approximate p-values for single-step GWAS in large populations, reducing computational costs while maintaining detection power.

The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

This study identifies key genes, miRNAs, and pathways in cardiac arrest using NGS data and bioinformatics analysis.

Accelerated dimensionality reduction of single -cell RNA sequencing data with fastglmpca

The paper introduces an efficient algorithm for dimensionality reduction of single-cell RNA-seq data, showing it outperforms existing methods in speed and quality.

Exploring the impacts of human breast milk functional lipidome on infant health and growth outcomes in early life using lipid bioinformatics

The paper uses lipid bioinformatics to analyze breast milk lipidomes and their association with infant health and growth outcomes.

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

This study compares censored least squares to other methods for imputing missing values in tensor factorization of biological datasets, finding censored least squares to be more accurate and efficient.