AIMC Topic: Caenorhabditis elegans
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Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.
PloS one
Aug 4, 2016
MOTIVATION: First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and...
A Model for Improving the Learning Curves of Artificial Neural Networks.
PloS one
Feb 22, 2016
In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural n...
CD-Based Indices for Link Prediction in Complex Network.
PloS one
Jan 11, 2016
Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value betwee...
Computer-Assisted Transgenesis of Caenorhabditis elegans for Deep Phenotyping.
Genetics
Jul 10, 2015
A major goal in the study of human diseases is to assign functions to genes or genetic variants. The model organism Caenorhabditis elegans provides a powerful tool because homologs of many human genes are identifiable, and large collections of geneti...
Computationally predicting protein-RNA interactions using only positive and unlabeled examples.
Journal of bioinformatics and computational biology
Feb 8, 2015
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...
Developmental time windows for axon growth influence neuronal network topology.
Biological cybernetics
Jan 30, 2015
Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers....
Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.
Journal of the American Chemical Society
Jul 2, 2025
Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a sample's microenvironment. However, data collection and interpretation are often challenging, and traditional methods ...
Advancing label-free cell classification with connectome-inspired explainable models and a novel LIVECell-CLS dataset.
Computers in biology and medicine
Jun 1, 2025
Deep learning label-free cell imaging has become essential in modern medical applications, enabling precise cell analysis while preserving natural biological functions and structures by removing the need for potentially disruptive staining reagents. ...
TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus ...
Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station.
Scientific data
May 10, 2025
Non-contact behavioral study through intelligent image analysis is becoming increasingly vital in animal neuroscience and ethology. The shift from traditional "black box" methods to more open and intelligent approaches is driven by advances in deep l...
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