BioBERT based text mining for incorporating prior knowledge in the inference of genetic network models.

Reconstruction of Gene Regulatory Networks (GRNs) is essential for understanding gene interactions, their impact on cellular processes, and manifestation of diseases, including drug discovery. Among various mathematical and dynamic models used for GRN reconstruction, S-system model, comprising non-linear differential equations, is widely utilised to capture the behaviour of complex biological systems with non-linear and time-dependent interactions. However, as the network size increases, computational demand for network inference grows due to a greater number of estimation parameters, significantly impacting the performance of optimisation algorithms.

Large language model based framework for automated extraction of genetic interactions from unstructured data.

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement.

Physics-Informed Explainable Continual Learning on Graphs.

Temporal graph learning has attracted great attention with its ability to deal with dynamic graphs. Although current methods are reasonably accurate, most of them are unexplainable due to their black-box nature. It remains a challenge to explain how temporal graph learning models adapt to information evolution.

MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks.

In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks.