deconvolution

Testing Hormone-specific Antibody Probes for Presumptive Detection and Separation of Contributor Cell Populations in Trace DNA Mixtures

This study tests hormone-specific antibody probes to label and isolate contributor cell populations in trace DNA mixtures, showing potential for improving DNA profiling success rates.

Robust representation of natural images by sparse and variable population of active neurons in visual cortex

The paper shows that natural images can be decoded from a small number of highly responsive neurons in V1, with reliable representation supported by overlapping receptive fields.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

The paper records neural activity in the motor cortex of people with paralysis during speech and non-speaking movements, showing that neurons modulate during speech and revealing common neural dynamics between speech and arm movements.

Addressing dereplication crisis: Taxonomy-free reduction of massive genome collections using embeddings of protein content

The paper introduces a method to reduce large genome collections by grouping similar genomes using embeddings, improving metagenomic classification accuracy.

A semi-supervised approach for cell phenotypic and functional estimation in tissue microenvironment

ICTD improves tissue microenvironment analysis by enhancing cell type identification, marker gene discovery, and functional inference through a semi-supervised deconvolution approach.

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

SCDC improves bulk RNA-seq deconvolution by integrating multiple scRNA-seq reference datasets, achieving better accuracy in cell-type decomposition.

Challenges in Peptide-Spectrum Matching: a Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits

The paper introduces PMD-FDR, a Bayesian confidence measure to filter high-scoring but likely false peptide-spectrum matches in proteomics, improving accuracy in standard, proteogenomics, and metaproteomics datasets.

Correction of missing-wedge artifacts in filamentous tomograms by template-based constrained deconvolution

This paper introduces a method to correct missing-wedge artifacts in filamentous tomograms using template-based constrained deconvolution, improving automated segmentation of structures like actin-filament bundles.

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

The paper introduces MIA-Sig, an algorithm to de-noise chromatin interaction data and identify topological domains and inter-domain contacts.

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

ADAPTS improves cell type deconvolution in single-cell RNAseq data by customizing signature matrices and using hierarchical clustering to refine cell type identification.

All-FIT: Allele-Frequency-based Imputation of Tumor Purity from High-Depth Sequencing Data

All-FIT estimates tumor purity from high-depth sequencing data by analyzing allele frequencies, outperforming existing methods in simulated and clinical data.

Quantum and Quantum-inspired Methods for de novo Discovery of Altered Cancer Pathways

This paper introduces quantum and quantum-inspired algorithms for discovering altered cancer pathways, showing they can identify known important pathways in AML using TCGA data.

genuMet: distinguish genuine untargeted metabolic features without quality control samples

genuMet identifies and reduces false metabolic features in untargeted metabolomics without pooled QC samples, achieving high true negative and true positive rates.

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

This paper introduces a sparse ensemble machine learning method to improve decoding robustness in iBMIs by reducing sensitivity to recording channel variations, achieving up to 24% accuracy improvement in non-human primates.

Moiety Modeling Framework for Deriving Moiety Abundances from Mass Spectrometry Measured Isotopologues

The paper introduces a framework for deconvolving isotopologue profiles using moiety models and provides Python packages for optimization and model selection.

De novo compartment deconvolution and weight estimation of tumor samples (DECODER)

DECODER deconvolves tumor samples to estimate compartment weights, showing clinical relevance in pancreatic cancer and applicability to other data types.

doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows

doepipeline optimizes bioinformatic software parameters using experimental design methods, improving outcomes compared to default settings in four use cases.

Cryptic Native American ancestry recapitulates population-specific migration and settlement of the continental United States

The paper analyzes Native American ancestry in three US populations, revealing distinct patterns linked to historical migration and settlement.

Data Denoising with transfer learning in single-cell transcriptomics

This paper improves scRNA-seq data quality using transfer learning by combining a deep autoencoder with a Bayesian model to extract gene-gene relationships across diverse datasets.

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

The paper evaluates and validates reference and library selection methods for deconvolving cord blood DNA methylation data, finding that the IDOL library outperforms automatic selection methods.

CRUMBLER: A tool for the Prediction of Ancestry in Cattle

CRUMBLER predicts cattle ancestry using SNP data and reference populations to estimate breed composition in admixed animals.

Tree Lab: Portable genomics for early detection of plant viruses and pests in Sub-Saharan Africa

This paper describes a portable genomics system for detecting plant viruses and pests in Sub-Saharan Africa using field-deployable devices.

The Bellerophon pipeline, improving de novo transcriptomes and removing chimeras

The paper introduces Bellerophon, a pipeline that improves de novo transcriptome quality by removing chimeric sequences through quality assessment and filtering steps.

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing For Aberrant Gene Expression Patterns

ICeD-T improves immune cell abundance estimation in tumor samples by handling aberrant gene expression patterns, outperforming existing methods in simulations and real data.

A deconvolution algorithm for multiecho functional MRI: Multiecho Sparse Paradigm Free Mapping

This paper introduces ME-SPFM, a deconvolution algorithm for multiecho fMRI that estimates transverse relaxation changes without prior event timing knowledge, showing high concordance with standard analysis.

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

BayICE improves gene expression deconvolution by addressing limitations in cell type gene selection and unknown cell profile recovery, showing better performance in estimating cell proportions in cancer data.

Modeling stimulus-dependent variability improves decoding of population neural responses

The paper shows that modeling stimulus-dependent variability improves neural decoding accuracy by using alternative models like CMP and NB instead of the Poisson model.

MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data.

MIXTURE improves immune tumor microenvironment estimation by reducing overestimation of cell-type counts and improving accuracy in bulk RNA data.

Real-Time Selective Sequencing with RUBRIC: Read Until with Basecall and Reference-Informed Criteria

The paper introduces RUBRIC, a real-time selective sequencing method for nanopore sequencing that uses on-line basecalling to align against references and make sequence/reject decisions, showing improved performance on mixed samples.

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

WikiMedMap maps phenotype strings to clinical vocabularies using Wikipedia, outperforming existing tools in matching accuracy.