AIMC Topic: Machine Learning

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AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.

Journal of chemical information and modeling
Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural...

Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling.

Journal of the American Chemical Society
Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that a...

Image Synthesis Pipeline for CNN-Based Sensing Systems.

Sensors (Basel, Switzerland)
The rapid development of machine learning technologies in recent years has led to the emergence of CNN-based sensors or ML-enabled smart sensor systems, which are intensively used in medical analytics, unmanned driving of cars, Earth sensing, etc. In...

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models.

Molecules (Basel, Switzerland)
Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pha...

Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes.

BioMed research international
This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes...

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images.

Journal of healthcare engineering
Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various...

Deep Learning in Healthcare System for Quality of Service.

Journal of healthcare engineering
Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the...

Bias-Eliminated Semantic Refinement for Any-Shot Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
When training samples are scarce, the semantic embedding technique, i. e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic...

Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk.

Environmental science and pollution research international
We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical ...

Testing a global null hypothesis using ensemble machine learning methods.

Statistics in medicine
Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is an important task in biomedical studies. We seek to improve the power of such testing methods by...