AIMC Topic: Computational Biology

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Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness.

Methods in molecular biology (Clifton, N.J.)
The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holisti...

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...

Using Gene Ontology to Annotate and Prioritize Microarray Data.

Methods in molecular biology (Clifton, N.J.)
The results of high-throughput experiments consist of numerous candidate genes, proteins, or other molecules potentially associated with diseases. A challenge for omics science is the knowledge extraction from the results and the filtering of promisi...

A Method for Localizing Non-Reference Sequences to the Human Genome.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
As the last decade of human genomics research begins to bear the fruit of advancements in precision medicine, it is important to ensure that genomics' improvements in human health are distributed globally and equitably. An important step to ensuring ...

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of pr...

Interpretable deep learning prediction of 3d assessment of cardiac function.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
As deep learning plays an increasing role in making medical decisions, explainability is playing an increasing role in satisfying regulatory requirements and facilitating trust and transparency in deep learning approaches. In cardiac imaging, the tas...

Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artifi...

Session introduction: AI-driven Advances in Modeling of Protein Structure.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The last few years mark dramatic improvements in modeling of protein structure. Progress was initially due to breakthroughs in residue-residue contact prediction, first with global statistical models and later with deep learning. These advancements w...

Vaccine Design by Reverse Vaccinology and Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction m...