AIMC Topic: Machine Learning

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Predicting Biological Activity from Biosynthetic Gene Clusters Using Neural Networks.

Journal of chemical information and modeling
Microorganisms such as bacteria and fungi have been used for natural products that translate to drugs. However, assessing the bioactivity of extract from culture to identify novel natural molecules remains a strenuous process due to the cumbersome or...

CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose commutative algebra machine learning (CAML) for the prediction of protein-ligand binding affinities. Spe...

Data-Driven Optimization of Industrial Impact Polypropylene Characterization: Machine Learning Insights.

Journal of chemical information and modeling
The experimental determination of impact polypropylene (ICP) physical properties, such as tensile modulus, flexural modulus, and impact strength, is a time-sensitive process that can delay real-time decision making during industrial production. This ...

EEG-based prediction of reaction time during sleep deprivation.

Sleep
Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics of neuronal networks in the brain and can even lead to hallucinations and epileptic seizures. In cogn...

Mid-level data fusion of pleural effusion SERS spectra and serum CEA levels using machine learning algorithms for precise lung cancer detection.

Nanoscale
Accurate identification of clinically malignant pleural effusions is critical for cancer diagnosis and subsequent treatment planning. Here, surface-enhanced Raman spectroscopy (SERS) data of pleural effusions and serum carcinoembryonic antigen (CEA) ...

Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.

Cancer biology & medicine
Natural products (NPs) have long been recognized for their therapeutic potential, especially in cancer treatment, due to an ability to interact with multiple cellular pathways. The identification of molecular targets for NPs is a critical step in und...

Transfer Learning for Designing Efficient Signal Peptides to Improve the Secretion Level of Recombinant Protein in .

Journal of agricultural and food chemistry
Signal peptides (SPs) play an essential role in determining the secretion efficiency of proteins of interest (POIs). However, the manual identification of SPs with a high secretion potential is both time-consuming and labor-intensive. Recently, many ...

Expanding biobank pharmacogenomics through machine learning calls of structural variation.

Genetics
Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multiuse resources, biobanks can facilitate diverse PGx resea...

Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.

Nucleic acids research
DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, ha...

Decoding Hidden Features in Near-Infrared Fluorescence Spectra of Single-Walled Carbon Nanotubes via Machine Learning for Multiplexed Virus Identification.

ACS nano
Single-walled carbon nanotubes (SWCNTs) exhibit rich spectral diversity in their near-infrared (nIR) fluorescence, offering strong potential for multiplexed optical sensing via diverse signal features, even with a single sensor. However, conventional...