AI Medical Compendium Topic:
Computational Biology

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Generative AI Models for the Protein Scaffold Filling Problem.

Journal of computational biology : a journal of computational molecular cell biology
De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches ...

mHPpred: Accurate identification of peptide hormones using multi-view feature learning.

Computers in biology and medicine
Peptide hormones were first used in medicine in the early 20th century, with the pivotal event being the isolation and purification of insulin in 1921. These hormones are integral to a sophisticated system that emerged early in evolution to regulate ...

Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and ...

Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing.

Methods (San Diego, Calif.)
Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination ...

Artificial intelligence and omics in malignant gliomas.

Physiological genomics
Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemothe...

Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.

International journal of environmental research and public health
Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influe...

Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of ch...

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

Methods (San Diego, Calif.)
HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limite...

BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis.

ACS synthetic biology
Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, na...

VotePLMs-AFP: Identification of antifreeze proteins using transformer-embedding features and ensemble learning.

Biochimica et biophysica acta. General subjects
Antifreeze proteins (AFPs) are a unique class of biomolecules capable of protecting other proteins, cell membranes, and cellular structures within organisms from damage caused by freezing conditions. Given the significance of AFPs in various domains ...