AIMC Topic: Reproducibility of Results

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VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.

International journal of molecular sciences
The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and th...

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

BioMed research international
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods wer...

Deep learning for cephalometric landmark detection: systematic review and meta-analysis.

Clinical oral investigations
OBJECTIVES: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometr...

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

CPT: pharmacometrics & systems pharmacology
We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). ...

Finding gene network topologies for given biological function with recurrent neural network.

Nature communications
Searching for possible biochemical networks that perform a certain function is a challenge in systems biology. For simple functions and small networks, this can be achieved through an exhaustive search of the network topology space. However, it is di...

Health improvement framework for actionable treatment planning using a surrogate Bayesian model.

Nature communications
Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominen...

A Review of Applications of Machine Learning in Mammography and Future Challenges.

Oncology
BACKGROUND: The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were s...

Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides.

Scientific reports
Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques includi...

Machine Learning Analysis of MicroRNA Expression Data Reveals Novel Diagnostic Biomarker for Ischemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Ischemic stroke (IS) is one of the leading causes of morbidity and mortality worldwide. Circulating microRNAs have a potential as minimally invasive biomarkers for disease prediction, diagnosis, and prognosis. In this study, we sought to ...