trajectory inference

Hierarchical Multi-Omics Trajectory Prediction forFecal Microbiota Transplantation: A Novel MachineLearning Framework for Small-Sample LongitudinalMulti-Omics Integration

This paper introduces HMOTP, a machine learning framework for predicting patient trajectories in small-sample longitudinal multi-omics studies, achieving high accuracy in FMT applications.

In vivo lineage tracing across human tissues using methylation barcodes in the protocadherin gene cluster

The paper uses methylation patterns in the protocadherin gene cluster to track clonal dynamics across human tissues over time, revealing subclonal architectures and cryptic expansions not detectable by standard mutation sequencing.

Development and Characterization of Self-Tracing Neural Progenitor Cells for Mapping Their Synaptic Integration into Endogenous Neural Networks

This paper develops self-tracing neural progenitor cells that express trans-synaptic tracers to trace integration into host neural networks in vitro and in vivo.

Discrete Diffusion for Single-Cell Gene Expression Modeling

This paper introduces Discrete Cell Models (DCM), a diffusion-based framework for single-cell gene expression modeling that operates in the discrete domain, achieving strong performance on benchmark datasets.

The Training Village: an open platform for continuous testing of rodents in cognitive tasks

The paper introduces the Training Village, an open-source automated system for rodent cognitive training, validated across tasks and species, showing efficient usage and stable engagement.

VDJdive and ECLIPSE enhance single-cell TCR sequencing analysis through the probabilistic resolution of ambiguous clonotypes

VDJdive and ECLIPSE resolve ambiguous clonotypes in scTCR-seq by improving chain pairing and clone size estimation, enhancing clonal tracking across conditions.

Resting-state Compensatory Remapping in Patients with Brain Tumour Before and After Surgery

This study uses resting-state functional connectivity to track network-level changes in brain tumour patients before and after surgery, finding persistent alterations and preoperative segregation predicting cognitive recovery.

Experimental Time Points Guided Transcriptomic Velocity Inference

CellDyc improves transcriptomic velocity inference by integrating experimental time points, enhancing trajectory reconstruction in development, disease, and reprogramming.

Learning a Continuous Progression Trajectory of Amyloid in Alzheimer's disease

This study introduces SLOPE, a method to model amyloid progression in Alzheimer's disease on a continuous trajectory, showing improved sensitivity to early changes compared to global measures.

Spaces and sequences in the hippocampus: a homological perspective

The paper uses path homology to analyze ordinal sequences in hippocampal activity during spatial navigation, finding most sequences are structurally equivalent and short motifs dominate.

Analysis of Transcriptograms in Epithelial-Mesenchymal Transition (EMT)

The paper introduces a method combining PPI network topology with PCA to analyze EMT, revealing hidden functional modules and distinct cellular trajectories.

SOX9 is part of a combinatorial marker that reveals early development and embryological origins of the mouse brown adipose tissue depots

The paper identifies the embryonic origins of major mouse brown adipose tissue depots using SOX9 as a marker and models their developmental trajectory.

Modeling mitochondrial inheritance enables high-precision single-cell lineage tracing in humans

MitoDrift models mitochondrial inheritance to enable high-precision lineage tracing in human tissues, revealing clonal dynamics and linking them to transcriptional and epigenetic programs.

Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland

The paper introduces OneCell CUT&Tag, a single-cell multi-omics method that captures chromatin, transcriptome, and surface marker data, revealing epigenomic reprogramming during mammary gland basal-to-luminal transition.

Primary and Supplementary Motor cortex implement parallel control solutions for rhythmic and discrete arm movements

This study shows that primate motor cortex uses both rhythmic and discrete movement control strategies in parallel, with M1 converging to a limit-cycle and SMA diverging before reaching a helical spiral.

Multimodal gene embeddings for drug-target prediction and lineage reconstruction

NEWT, a multimodal deep learning framework, improves drug-target prediction and lineage reconstruction by integrating diverse biological data into a unified representation space.

Genome-wide in vivo dynamics of cohesin-mediated loop extrusion and its role in transcription activation

This study investigates cohesin-mediated loop extrusion dynamics and its role in transcription activation by depleting NIPBL in vivo.

evoCancerGPT: Generating Zero-Shot Single-Cell and Single-Sample Cancer Progression Through Transfer Learning

evoCancerGPT generates cancer progression patterns using transfer learning on single-cell RNA sequencing data, outperforming baselines in low-context scenarios.

PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns

PreTSA efficiently models temporal and spatial gene expression patterns in large single-cell and spatial transcriptomics datasets, matching state-of-the-art results with reduced computational time.

A dorsal hippocampus-prodynorphinergic dorsolateral septum-to-lateral hypothalamus circuit mediates contextual gating of feeding

The paper identifies a circuit involving DLS Pdyn neurons that links contextual cues to feeding regulation through inhibition of LHA GABAergic neurons.

SpatialESD: spatial ensemble domain detection in spatial transcriptomics

SpatialESD improves spatial domain detection in spatial transcriptomics by integrating multiple methods and capturing both direct and indirect relationships between clusters.

Compositional decoding of neural activity enhances generalization in handwriting BCIs

This paper introduces a compositional decoding model for handwriting BCIs that improves generalization by leveraging behavioral motifs.

Rhythmic sampling of multiple decision alternatives in the human brain

The paper shows that human brains rhythmically sample decision alternatives at 11 Hz, with attention shifts occurring at troughs and focus deepening at peaks.

Tension shapes memory: Computational insights into neural plasticity

This paper presents a spiking neural network model showing how mechanical tension affects memory recall speed, inter-regional synchrony, and memory association in neural networks.

A spatiotemporal immune atlas of subarachnoid hemorrhage from single-cell and spatial transcriptomics

This paper constructs a spatiotemporal immune atlas of subarachnoid hemorrhage using single-cell and spatial transcriptomics to identify immune cell subsets and their dynamics.

HiCInterpolate: 4D Spatiotemporal Interpolation of Hi-C Data for Genome Architecture Analysis.

HiCInterpolate interpolates Hi-C contact matrices between timestamps to generate high-resolution 3D genome structures, preserving key chromatin features.

Dissecting reversible and irreversible single cell state transitions from gene regulatory networks

This paper introduces a method to predict reversible and irreversible cell state transitions using gene regulatory networks and gene expression data.

Sex-Specific Regulation of Endothelial-to-Mesenchymal Transition (EndMT) During Atherosclerosis

The paper examines sex-specific regulation of EndMT in atherosclerosis using bulk RNA-seq and single-cell transcriptomics, identifying key regulators and stage-dependent differences.

Temporal-orbitofrontal pathway regulates choices across physical reward and visual novelty

The paper investigates how perceptual novelty and reward value interact in decision-making by identifying a neural pathway between the anterior ventral temporal cortex and orbitofrontal cortex.

Estimating Gait Kinematics from Muscle Activity Using Deep Learning in Typically Developing Children

This study uses a 1D U-Net to estimate ankle and knee joint angles from sEMG signals in children, showing improved accuracy with age and stability during gait transitions.