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PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. ...

The Rational Irrational: Better Learners Show Stronger Frequency Heuristics

Does favoring less valuable options that deliver more frequent rewards reflect flawed decision-making or an adaptive strategy under complex environments? Frequency effects, defined as a bias toward more frequently rewarded but less valuable options, have traditionally been viewed as maladaptive decision-making deficits. In the present study, we used a within-subject design in which participants co...

Steering Vector Fields for Property-Controlled Molecular Generation with Chemical Language Models

Chemical language models have recently become a powerful tool for the de novo generation of drug-like molecules represented as SMILES strings. A centr...

Leveraging the largest harmonized epigenomic data collection for metadata prediction validated and augmented over 350,000 public epigenomic datasets

Epigenomic data found in public databases often suffer from issues of non-standardization and incompleteness in their associated metadata. There are c...

Brain-like variability in convolutional neural networks reveals evidence-, uncertainty- and bias-driven decision-making

Even when stimuli and tasks are held constant, brain activity fluctuates markedly across trials, yet it is not well understood how these fluctuations ...

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet th...

Improved interpretability in LFADS models using a learned, context-dependent per-trial bias

The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal...

Computer Vision Methods for Spatial Transcriptomics: A Survey

Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprec...

ZebraTrack: An Open-Source Object Detection Algorithm to Detect and Track Larval Zebrafish Motor Touch Responses

Zebrafish (Danio rerio) are a model organism used for the study of vertebrate development, disease and drug discovery. Two-day old larval zebrafish ex...

Automated Facial Landmark Analysis vs. Manual Coding: Accuracy in Dog Emotional Expression Classification

Emotional expression in dogs is central to dog-human interactions. Reliable indicators are essential for interpreting animal emotions; however, their ...

Dynamic and task-dependent decoding of the human attentional spotlight from MEG

Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive elec...

Rapidly Reconfigurable Dynamic Computing in Neural Networks with Fixed Synaptic Connectivity

Learning and memory in the brain’s neocortex have long been hypothesised to be primarily mediated by synaptic plasticity. Extensive research in artifi...

Computational Characterization of Decision Making During Trans-saccadic Visual Perception

When sampling visual information from the environment, humans execute fast sequential saccadic eye movements and yet preserve stability in their visua...

Assessment of Visual Function in Mice Using Light/Dark Box and Multi-Feature Machine Learning

The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay...

Mapping the AI Life Sciences Landscape in Greece: A National Survey and Bibliometric Comparison with Global Trends

Artificial intelligence is increasingly used in Life Sciences, though the pace and direction of adoption varies widely across countries. To map the Gr...

OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing targ...

Deep learning to overcome human error and bias in electrode position extraction in tDCS-fMRI studies

Combining transcranial direct current stimulation (tDCS) with fMRI enables investigation of stimulation effects, while structural MRI verifies electro...

Non-invasive vagus nerve stimulation modulates Pavlovian bias in a state-dependent manner

The vagus nerve transmits vital signals between organ systems of the body and the brain. Despite growing interest in non-invasive transcutaneous vagus...

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction err...

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousness

Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standar...

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