AI Medical Compendium Topic:
Computational Biology

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CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

IEEE journal of biomedical and health informatics
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the e...

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

IEEE journal of biomedical and health informatics
CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disea...

Identification of LPCAT1 as a key biomarker for Crohn's disease based on bioinformatics and machine learnings and experimental verification.

Gene
Epithelial-mesenchymal transition (EMT) plays a crucial role in regulating inflammatory responses and fibrosis formation. This study aims to explore the molecular mechanisms of EMT-related genes in Crohn's disease (CD) through bioinformatics methods ...

mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Methods (San Diego, Calif.)
Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few ex...

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Computers in biology and medicine
Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costl...

Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...

A systematic analysis of regression models for protein engineering.

PLoS computational biology
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

PLoS computational biology
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Ye...

Automated annotation of disease subtypes.

Journal of biomedical informatics
BACKGROUND: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications...

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning.

Aging
Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung c...