AI Medical Compendium Journal:
Methods in molecular biology (Clifton, N.J.)

Showing 1 to 10 of 269 articles

Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.

Methods in molecular biology (Clifton, N.J.)
Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since ...

Predictive Machine Learning Models for Olfaction.

Methods in molecular biology (Clifton, N.J.)
A classical problem in neuroscience, biology, and chemistry is linking the chemical structure of odorants to their olfactory perception. This difficulty arises from the subjective nature of odor perception, incomplete understanding of the physiologic...

Deep Learning-Based Image Restoration and Super-Resolution for Fluorescence Microscopy: Overview and Resources.

Methods in molecular biology (Clifton, N.J.)
Fluorescence microscopy is a key method for the visualization of cellular, subcellular, and molecular live-cell dynamics, enabling access to novel insights into mechanisms of health and disease. However, effects like phototoxicity, the fugitive natur...

A Machine Learning Pipeline to Screen Large In Vivo Molecular Data to Curate Disease Signatures of High Translational Potential.

Methods in molecular biology (Clifton, N.J.)
A significantly low success rate of human clinical studies has long been attributed to a capability gap, namely, an ineffective translation of the animal data to the human context. To bridge this capability gap, several correcting measures have been ...

Applying AI/ML for Analyzing Gene Expression Patterns.

Methods in molecular biology (Clifton, N.J.)
Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomics is not matching the levels others have achieved. Challenges include but are not limited to the ha...

Artificial Intelligence Methods in Infection Biology Research.

Methods in molecular biology (Clifton, N.J.)
Despite unprecedented achievements, the domain-specific application of artificial intelligence (AI) in the realm of infection biology was still in its infancy just a couple of years ago. This is largely attributable to the proneness of the infection ...

A Deep Learning and PSSM Profile Approach for Accurate SNARE Protein Prediction.

Methods in molecular biology (Clifton, N.J.)
SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach emp...

Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.

Methods in molecular biology (Clifton, N.J.)
The interactions between long noncoding RNA (lncRNA) and protein play crucial roles in various biological processes. Computational methods are essential for predicting lncRNA-protein interactions and deciphering their mechanisms. In this chapter, we ...

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurr...

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Methods in molecular biology (Clifton, N.J.)
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularl...