AIMC Topic: Machine Learning

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Machine learned coarse-grained protein force-fields: Are we there yet?

Current opinion in structural biology
The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning...

Prediction of mustard yield using different machine learning techniques: a case study of Rajasthan, India.

International journal of biometeorology
Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yiel...

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Molecules (Basel, Switzerland)
Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural net...

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.

Journal of biomedical semantics
BACKGROUND: Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specif...

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications.

Scientific reports
A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest...

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.

Scientific reports
Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulat...

Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye.

Environmental science and pollution research international
With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting flood...

An interpretable machine learning approach to multimodal stress detection in a simulated office environment.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providin...

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Journal of chemical information and modeling
Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a l...

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach.

International journal of environmental research and public health
Studies of life satisfaction in older adults have been conducted extensively through empirical research, questionnaires, and theoretical analysis, with the majority of these studies basing their analyses on simple linear relationships between variabl...