AIMC Topic: Neural Networks, Computer

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Structure-based drug design with geometric deep learning.

Current opinion in structural biology
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...

: Quantification of user-defined animal behaviors using learning-based holistic assessment.

Cell reports methods
Quantifying animal behavior is important for biological research. Identifying behaviors is the prerequisite of quantifying them. Current computational tools for behavioral quantification typically use high-level properties such as body poses to ident...

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning.

Biosensors
Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very smal...

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.

Sensors (Basel, Switzerland)
Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. C...

PM2.5 Concentration Prediction Model: A CNN-RF Ensemble Framework.

International journal of environmental research and public health
Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction a...

Statistical models versus machine learning for competing risks: development and validation of prognostic models.

BMC medical research methodology
BACKGROUND: In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growi...

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Scientific reports
Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used m...

Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing.

Nature communications
Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore's law. However, an ideal artificial neuron possessing bio-inspi...

A deep learning system for heart failure mortality prediction.

PloS one
Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid...

Marine ship instance segmentation by deep neural networks using a global and local attention (GALA) mechanism.

PloS one
Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships...