AIMC Topic: Systems Biology

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Prediction of whole-cell transcriptional response with machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we prese...

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.

Methods in molecular biology (Clifton, N.J.)
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are...

Computational Systems Biology and Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
Aware of the rapid evolution of computational systems biology (CSB), which is the focus of this book, we address the emergence of artificial intelligence (AI). Consequently, one of the main purposes of this Introduction is to assess where the relatio...

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...

Training with Small Medical Data: Robust Bayesian Neural Networks for Colon Cancer Overall Survival Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fast and accurate cancer prognosis stratification models are essential for treatment designs. Large labeled patient data can power advanced deep learning models to obtain precise predictions. However, since fully labeled patient data are hard to acqu...

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease.

Database : the journal of biological databases and curation
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders'...

Ciliate behavior: blueprints for dynamic cell biology and microscale robotics.

Molecular biology of the cell
Place a drop of pond water under the microscope, and you will likely find an ocean of extraordinary and diverse single-celled organisms called . This remarkable group of single-celled organisms wield microtubules, active systems, electrical signaling...

Assessing the Anti-cancer Therapeutic Mechanism of a Herbal Combination for Breast Cancer on System-level by a Network Pharmacological Approach.

Anticancer research
BACKGROUND/AIM: Accumulating evidence has shown therapeutic effects of herbals on breast cancer, a commonly diagnosed malignancy in women worldwide. However, their underlying mechanisms remain unclear. We aimed to explore the mode of action of a rece...

Multi-omics integration-a comparison of unsupervised clustering methodologies.

Briefings in bioinformatics
With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these f...

Dissecting celastrol with machine learning to unveil dark pharmacology.

Chemical communications (Cambridge, England)
By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the anti...