AIMC Topic: Computational Biology

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CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.

Interdisciplinary sciences, computational life sciences Sep 30, 2024
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...
Anti-Bacterial Agents Bacteria Machine Learning Evolution, Molecular Multigene Family Genome, Bacterial Computational Biology Biological Products
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DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network.

International journal of biological macromolecules Sep 30, 2024
Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation. However, these drugs often come with numerous side effects. Therefore, finding more effective metho...
Anti-Inflammatory Agents Deep Learning Humans Computational Biology Peptides
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Predicting RNA sequence-structure likelihood via structure-aware deep learning.

BMC bioinformatics Sep 30, 2024
BACKGROUND: The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many rese...
RNA Nucleic Acid Conformation Deep Learning Neural Networks, Computer Computational Biology Sequence Analysis, RNA
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Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.

Scientific reports Sep 30, 2024
The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensiti...
Software Algorithms Computational Biology Mutation Neoplasms Humans Machine Learning
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Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.

PLoS computational biology Sep 30, 2024
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the...
Epidemiological Models Spatio-Temporal Analysis Deep Learning Computational Biology Disease Susceptibility China Humans Neural Networks, Computer COVID-19 SARS-CoV-2
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Protein Engineering Protein Binding Computational Biology Deep Learning Models, Molecular Humans Receptors, Antigen, T-Cell Protein Conformation
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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.

Molecules (Basel, Switzerland) Sep 29, 2024
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computati...
Humans Computational Biology Machine Learning Models, Molecular Artificial Intelligence Proteins Drug Design Protein Engineering
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PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.

Communications biology Sep 28, 2024
Identifying anti-inflammatory peptides (AIPs) and antimicrobial peptides (AMPs) is crucial for the discovery of innovative and effective peptide-based therapies targeting inflammation and microbial infections. However, accurate identification of AIPs...
Antimicrobial Peptides Anti-Inflammatory Agents Neural Networks, Computer Computational Biology Amino Acid Sequence
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Wfold: A new method for predicting RNA secondary structure with deep learning.

Computers in biology and medicine Sep 27, 2024
Precise estimations of RNA secondary structures have the potential to reveal the various roles that non-coding RNAs play in regulating cellular activity. However, the mainstay of traditional RNA secondary structure prediction methods relies on thermo...
Nucleic Acid Conformation Sequence Analysis, RNA RNA Humans Deep Learning Software Computational Biology
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Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Nature communications Sep 27, 2024
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To ad...
Humans Intrinsically Disordered Proteins Mutation, Missense Phase Separation Machine Learning Computational Biology
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