AI Medical Compendium Topic:
Computational Biology

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Deep learning methods for protein function prediction.

Proteomics
Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in p...

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome.

HGG advances
Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied proble...

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific mechanisms of IDD remain unclear. This study aims to identify key genes and pathways associated with I...

Vaccine design and development: Exploring the interface with computational biology and AI.

International reviews of immunology
Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the t...

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Omics : a journal of integrative biology
Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust system...

Multi-omics based artificial intelligence for cancer research.

Advances in cancer research
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity ...

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.

Journal of computational biology : a journal of computational molecular cell biology
Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively us...

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks.

Analytical biochemistry
The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conven...

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

Interdisciplinary sciences, computational life sciences
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs ...