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11/11/2025
research article
As a well-established and extensively utilized model organism, C. elegans serves as a crucial platform for investigating behavioral regulation mechanisms and their biological significance. However, manually tracking the locomotor behavior of large numbers of C. elegans is both cumbersome and inefficient. The authors propose an automated approach for analyzing C. elegans behavior through deep learning-based tracking.
Image credit: Wikimedia
Automated C. elegans behavior analysis via deep learning-based detection and tracking
Recently Published Articles
11/03/2025
research article
Genome-wide scans using small RNA-seq data often yield high rates of false-positive MIRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating new MIRNA annotations using sRNA-seq data.
miRScore: A rapid and precise microRNA validation tool
Image credit: Pawel Czerwinski
11/07/2025
research article
Biological systems often evolve across multiple time scales, posing major challenges for constructing accurate models directly from data. Traditional model reduction techniques require explicit equations and thus cannot be applied when only observational data are available.
Data-driven identification of biological systems using multi-scale analysis
Image credit: Nathan Andersen
09/11/2025
research article
There is growing interest in pre-training antibody language models (AbLMs) with a mixture of unpaired and natively paired sequences, seeking to combine the proven benefits of training with natively paired sequences with the massive scale of unpaired antibody sequence datasets.
A curriculum learning approach to training antibody language models
Image credit: Pavel Czerwinski
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This Collection aims to increase the coverage of computational biology-related topics in Wikipedia by rewarding authors with a citable, PubMed-indexed static version of the living document on Wikipedia.
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This series of perspective articles from computational biologists in a variety of countries, offers their personal viewpoint on the history, current status, and future of computational biology in their region.
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This collection aims to provide practical tutorials and background information on important computational methods used to investigate key biological questions, as well as resources for training scientists at all stages of their careers.
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The Benchmarking collection help readers decide between a number of methods which are developed to solve scientific problems.
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