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

Showing 91 to 100 of 269 articles

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Methods in molecular biology (Clifton, N.J.)
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage...

Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation.

Methods in molecular biology (Clifton, N.J.)
Protein posttranslational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functionals PTMs in the eukaryotic proteome is currently underes...

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Methods in molecular biology (Clifton, N.J.)
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. He...

A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction.

Methods in molecular biology (Clifton, N.J.)
Phosphorylation plays a vital role in signal transduction and cell cycle. Identifying and understanding phosphorylation through machine-learning methods has a long history. However, existing methods only learn representations of a protein sequence se...

Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma.

Methods in molecular biology (Clifton, N.J.)
Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently patholo...

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.)
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...

Predicting Type III Effector Proteins Using the Effectidor Web Server.

Methods in molecular biology (Clifton, N.J.)
Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically...

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.

Methods in molecular biology (Clifton, N.J.)
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are...

Computational Systems Biology and Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
Aware of the rapid evolution of computational systems biology (CSB), which is the focus of this book, we address the emergence of artificial intelligence (AI). Consequently, one of the main purposes of this Introduction is to assess where the relatio...

Deep Mining from Omics Data.

Methods in molecular biology (Clifton, N.J.)
Since the advent of high-throughput omics technologies, various molecular data such as genes, transcripts, proteins, and metabolites have been made widely available to researchers. This has afforded clinicians, bioinformaticians, statisticians, and d...