AIMC Topic: Computational Biology

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Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?

Health security
In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol threats to be "not feasible for daily operations" due to the cost of reagents used for metagenomics, cost of sequencing instruments, and cost of labor f...

Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.

MicroRNA (Shariqah, United Arab Emirates)
BACKGROUND: Long non-coding RNA (lncRNA) plays a crucial role in various biological processes, and mutations or imbalances of lncRNAs can lead to several diseases, including cancer, Prader-Willi syndrome, autism, Alzheimer's disease, cartilage-hair h...

Contribution of Artificial Intelligence to the Identification of Protein-Protein Interactions: A Case Study on PAR-3 and Its Partner Adapter Molecule Crk.

Methods in molecular biology (Clifton, N.J.)
Protein-protein interactions (PPIs) are known to be involved in most cellular functions, and a detailed knowledge of such interactions is essential for studying their role in normal and pathological conditions. Significant progress is being made in t...

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

Current pharmaceutical design
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying ...

LARGE LANGUAGE MODELS (LLMS) AND CHATGPT FOR BIOMEDICINE.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Large Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate n...

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These m...

Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer ti...

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models ma...

Optimizing Computer-Aided Diagnosis with Cost-Aware Deep Learning Models.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Classical machine learning and deep learning models for Computer-Aided Diagnosis (CAD) commonly focus on overall classification performance, treating misclassification errors (false negatives and false positives) equally during training. This uniform...

Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficient tools for assessing cardiac function. AI diagnostics claims to improve upon the human-to-human var...