deconvolution

SNP-based quantitative deconvolution of biological mixtures: application to the detection of cows with subclinical mastitis by whole genome sequencing of tank milk.

The paper presents a SNP-based method to deconvolute biological mixtures using whole genome sequencing, applied to detect subclinical mastitis in cows via tank milk analysis.

Novel neural signal features permit robust machine-learning of natural tactile- and proprioception-dominated dorsal column nuclei signals

The paper identifies neural signal features that improve decoding of somatosensory stimuli from dorsal column nuclei in rats, showing better generalisation for proprioception-dominated signals.

Deep-learning-based cell composition analysis from tissue expression profiles.

Scaden is a deep learning model for cell deconvolution that outperforms existing methods and works across bulk RNA-seq, microarray, and human/mouse tissue data.

A smart polymer for sequence-selective binding, pulldown and release of DNA targets

This paper introduces a methanol-responsive polymer (MeRPy) that selectively binds and releases DNA targets, improving NGS library quality by depleting unwanted transcripts.

Related Topics of a Novel TCR-based Cancer Detection Approach

This paper introduces DeepCAT, a CNN-based method for detecting cancer-associated TCRs, showing improved accuracy over TCRboost and robustness to variable CDR3 lengths and inflammatory conditions.

DEBay: a computational tool for deconvolution of quantitative PCR data for estimation of cell type-specifc gene expression in a mixed population

DEBay estimates cell type-specific gene expression from mixed qPCR data using Bayesian methods and provides a GUI and source code.

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

The paper introduces Decon2, a method to deconvolute bulk blood eQTL effects into immune cell subpopulations, showing high concordance with experimental and single-cell studies.

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

The paper improves rangeland fractional cover predictions using multitask learning and provides pixel-level uncertainty estimates, enhancing management tools with better accuracy and confidence measures.

Decoding neural signals and discovering their representations with a compact and interpretable convolutional neural network

This paper introduces a compact convolutional neural network for decoding ECoG signals into finger movements and provides a method to interpret the network's spatial and temporal weights.

Masserstein: robust linear deconvolution by optimal transport

The paper introduces a linear deconvolution method using optimal transport to improve accuracy in mass spectrometry by reducing sensitivity to measurement inaccuracies.

UltraPrep is a scalable, cost-effective, bead-based method for purifying cell-free DNA

UltraPrep is a cost-effective, bead-based method for purifying cell-free DNA that improves quality and reduces costs compared to existing methods.

Epigenetic heterogeneity after de novo assembly of native full-length Hepatitis B Virus genomes

The paper develops a method to detect 5mCpG in HBV genomes without bisulfite conversion, enabling de novo assembly and identification of epigenetic clusters in infected patients.

Verification of CRISPR editing and finding transgenic inserts by Xdrop Indirect sequence capture followed by short- and long- read sequencing

The paper validates CRISPR-Cas9 editing and identifies transgenic inserts using Xdrop for targeted enrichment followed by short- and long-read sequencing, revealing previously undetected large insertions.

Development of novel apoptosis-assisted lung tissue decellularization methods

This paper develops an apoptosis-assisted decellularization method for lung tissue that improves ECM preservation and cell removal compared to detergent-based methods.

Identifying Ashkenazi Jewish BRCA1/2 founder variants in individuals who do not self-report Jewish ancestry

The paper identifies Ashkenazi Jewish BRCA1/2 founder variants in individuals without self-reported Jewish ancestry, showing many lack ancestry reporting but have detectable genetic ancestry and may need BRCA testing.

Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

This study uses deep learning to analyze bone marrow histopathology in MDS patients, linking morphological features to genetic and clinical outcomes.

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Bisque estimates cell composition in bulk RNA-seq using single-cell data, improving accuracy and efficiency over existing methods.

Exploiting Electrophysiological Measures of Semantic Processing for Auditory Attention Decoding

The paper investigates whether combining semantic dissimilarity features with auditory envelope features improves decoding of auditory attention in multi-speaker contexts, finding a marginal improvement that may not justify the added complexity.

Internal Control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

The paper introduces an internal control method for metagenomic next-generation sequencing of urine samples to improve process control and reduce technical bias.

A two-stage ECG signal denoising method based on deep convolutional network

This paper proposes a two-stage deep learning method for ECG denoising using U-net and DR-net models, showing improved performance over single-stage methods.

Anterograde Trans-Synaptic AAV Strategies for Probing Neural Circuitry

This study demonstrates that AAV1 preferentially spreads transsynaptically through glutamatergic and GABAergic synapses, enabling the labeling of neurons based on their input sources and molecular identities.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

The paper develops methods to deconvolve tumor copy number data by integrating bulk and single-cell sequencing data, improving accuracy over bulk data alone.

Amplitude Analysis of Polarization Modulation Data and 3D-Polarization Demodulation (3D-SPoD)

This paper analyzes the amplitude of polarization modulation data in 3D-SPoD to improve structure reconstruction and reduce background noise in super-resolution imaging.

ProteoClade: a taxonomic toolkit for multi-species and metaproteomic analysis

ProteoClade processes multi-species proteomic data for taxa-specific analysis and quantitation, enabling efficient workflows for complex microbiota samples.

FLASHDeconv: Ultrafast, high-quality feature deconvolution for top-down proteomics

FLASHDeconv improves feature deconvolution in top-down proteomics by accelerating the process and enhancing quality.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

This study evaluates DNA methylation deconvolution methods for cell-type proportion inference, identifies critical steps for accuracy, and develops a benchmark pipeline.

Comprehensive benchmarking of computational deconvolution of transcriptomics data

This study benchmarks deconvolution methods for bulk transcriptomics data, showing reference marker selection and normalization impact performance, with single-cell methods performing comparably to bulk methods.

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

The paper develops BayesPrism, a Bayesian method to infer cellular composition and gene expression from bulk RNA-seq using scRNA-seq as prior, revealing tumor-microenvironment interactions across multiple cancer types.

Optimization of subsampling, decontamination, and DNA extraction of difficult peat and silt permafrost samples

This paper evaluates decontamination and DNA extraction methods for peat and silt permafrost samples, finding that a combined bleaching, washing, and scraping method effectively removes contamination and a modified DNA extraction kit yields amplifiable DNA.

A Pairwise Distance Distribution Correction (DDC) algorithm to eliminate blinking-caused artifacts in super-resolution microscopy

The paper introduces DDC, a method to correct blinking artifacts in SMLM by using true pairwise distance distributions, improving spatial reconstruction accuracy.