AIMC Topic: Machine Learning

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Development and validation of a machine-learning model to predict lymph node metastasis of intrahepatic cholangiocarcinoma: A retrospective cohort study.

Bioscience trends
Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This study involved patients who underwent curative liver resection between different time periods. Three ma...

Interpretable and explainable predictive machine learning models for data-driven protein engineering.

Biotechnology advances
Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein ...

Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.

International journal of antimicrobial agents
BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward i...

Novel method for predicting concentrations of incineration flue gas based on waste composition and machine learning.

Journal of environmental management
The complex composition of solid waste leads to the variability of flue gas emissions during its incineration, which poses a challenge to the stable operation of incineration and pollution control systems. To address this problem, the study explored ...

Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.

Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology
This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning interven...

Integrating Chemical Mechanisms and Feature Engineering in Machine Learning Models: A Novel Approach to Analyzing HONO Budget.

Environmental science & technology
Nitrous acid (HONO) serves as the primary source of OH radicals in the atmosphere, exerting significant impacts on atmospheric secondary pollution. The heterogeneous reactions of NO on surfaces and photolysis of particulate nitrate or adsorbed nitric...

Sulfuric Acid-Driven Nucleation Enhanced by Amines from Ethanol Gasoline Vehicle Emission: Machine Learning Model and Mechanistic Study.

Environmental science & technology
The sulfuric acid (SA)-amine nucleation mechanism gained increasing attention due to its important role in atmospheric secondary particle formation. However, the intrinsic enhancing potential (IEP) of various amines remains largely unknown, restraini...

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...

Machine Learning and Deep Learning Approaches for Arabic Sign Language Recognition: A Decade Systematic Literature Review.

Sensors (Basel, Switzerland)
Sign language (SL) is a means of communication that is used to bridge the gap between the deaf, hearing-impaired, and others. For Arabic speakers who are hard of hearing or deaf, Arabic Sign Language (ArSL) is a form of nonverbal communication. The d...

Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning.

Sensors (Basel, Switzerland)
Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need ...