AIMC Topic: Machine Learning

Clear Filters Showing 11841 to 11850 of 34417 articles

Exploring machine learning for untargeted metabolomics using molecular fingerprints.

Computer methods and programs in biomedicine
BACKGROUND: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical researc...

Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Multiple sclerosis and related disorders
Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neu...

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.

The Science of the total environment
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Rec...

Machine learning approaches to predict TAS2R receptors for bitterants.

Biotechnology and bioengineering
Bitter taste involves the detection of diverse chemical compounds by a family of G protein-coupled receptors, known as taste receptor type 2 (TAS2R). It is often linked to toxins and harmful compounds and in particular bitter taste receptors particip...

Computer-aided design space identification for screening of protein A affinity chromatography resins.

Journal of chromatography. A
The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best pr...

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

Computers in biology and medicine
OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist...

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Journal of chemical information and modeling
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providi...

Predicting the effect of chemicals on fruit using graph neural networks.

Scientific reports
The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas where data is unstructured, traditional machine learni...