AIMC Topic: Neural Networks, Computer

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Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement.

Scientific reports
Leukocytes or white blood cells (WBCs) are the main components of the immune system that protect the human body from various infections caused by viruses, bacteria, fungi, and other microorganisms. There are five major types of leukocytes: basophils,...

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories.

Journal of chemical information and modeling
With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation data sets has emerged. By leveraging AutoEncoders, a type of artific...

Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.

BMJ health & care informatics
OBJECTIVE: To evaluate the transferability of BERT (Bidirectional Encoder Representations from Transformers) to patient safety, we use it to classify incident reports characterised by limited data and encompassing multiple imbalanced classes.

Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.

Biochemistry
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to accumulate mutations in the spike receptor-binding domain (RBD) region, leading to the emergence of new variants that potentially change the binding affinity for the human angi...

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.

Journal of chemical information and modeling
Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at...

Small sample data-driven interpretable artificial neural network computation for two-component chromatographic separation process.

Journal of chromatography. A
The design and calculation of chromatographic separation processes are often achieved by chromatographic models. When the adsorption mechanism is complex and the adsorption relationship is difficult to determine, the application effect of mechanism-d...

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction.

Journal of chemical information and modeling
Accurate modeling of protein-peptide interactions is essential for understanding fundamental biological processes and designing peptide-based drugs. However, predicting the complex structures of these interactions remains challenging, primarily due t...

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Scientific reports
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstand...

Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.

A&A practice
BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predictin...

C-net: Cross-organ cross-modality cswin-transformer coupled convolutional network for dual task transfer learning in lymph node segmentation and classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning has made notable strides in the ultrasonic diagnosis of lymph nodes, yet it faces three primary challenges: a limited number of lymph node images and a scarcity of annotated data; difficulty in comprehensively learning both local and gl...