AIMC Topic: Algorithms

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Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Investigative radiology
PURPOSE: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT...

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

European journal of clinical nutrition
BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which pat...

Generative AI Models for the Protein Scaffold Filling Problem.

Journal of computational biology : a journal of computational molecular cell biology
De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches ...

RFNet: Multivariate long sequence time-series forecasting based on recurrent representation and feature enhancement.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series exhibit complex patterns and structures involving interactions among multiple variables and long-term temporal dependencies, making multivariate long sequence time series forecasting (MLSTF) exceptionally challenging. Despite...

HSTrans: Homogeneous substructures transformer for predicting frequencies of drug-side effects.

Neural networks : the official journal of the International Neural Network Society
Identifying the frequencies of drug-side effects is crucial for assessing drug risk-benefit. However, accurately determining these frequencies remains challenging due to the limitations of time and scale in clinical randomized controlled trials. As a...

FusionOC: Research on optimal control method for infrared and visible light image fusion.

Neural networks : the official journal of the International Neural Network Society
Infrared and visible light image fusion can solve the limitations of single-type visual sensors and can boost the target detection performance. However, since the traditional fusion strategy lacks the controllability and feedback mechanism, the fusio...

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.

Journal of molecular graphics & modelling
A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selecti...

Lung nodule classification using radiomics model trained on degraded SDCT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challeng...

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods.

Computers in biology and medicine
Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low...

Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and ...