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A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition
BackgroundThe biomedical literature is growing rapidly, and it is increasingly important to extract meaningful information from the vast amount of...
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Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenoty** of plants
High-throughput phenoty** is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenoty**...
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Analysis of RNA-Seq data using self-supervised learning for vital status prediction of colorectal cancer patients
BackgroundRNA sequencing (RNA-Seq) is a technique that utilises the capabilities of next-generation sequencing to study a cellular...
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ProtPlat: an efficient pre-training platform for protein classification based on FastText
BackgroundFor the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods...
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Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review
Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their...
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Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in...
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Deep learning algorithms applied to computational chemistry
Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance...
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A comparative analysis of deep learning methods for weed classification of high-resolution UAV images
Because weeds compete directly with crops for moisture, nutrients, space, and sunlight, their monitoring and control is an essential necessity in...
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Multiplicative processing in the modeling of cognitive activities in large neural networks
Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since...
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A passion fruit counting method based on the lightweight YOLOv5s and improved DeepSORT
Accurate yield estimation of passion fruits is essential for planning acreage and harvest timing. However, due to the complexity of the natural...
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A novel approach for underwater fish segmentation in complex scenes based on multi-levels triangular atrous convolution
Underwater segmentation technology achieves effective monitoring of fish biological information through accurate identification of fish species and...
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CNN-based two-branch multi-scale feature extraction network for retrosynthesis prediction
BackgroundRetrosynthesis prediction is the task of deducing reactants from reaction products, which is of great importance for designing the...
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Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning
Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins....
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Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning
Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy features, which can lead to...
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Value Chains Sustainability Through the Biorefinery Concept: The Colombian Case
Some value chains (VCs) currently represent bioeconomy systems through which countries have opportunities for development and growth. However, not...
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Brains and algorithms partially converge in natural language processing
Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those...
