Identifying key driver genes governing biological processes such as development and disease progression remains a challenge. While existing methods can reconstruct cellular trajectories or infer static gene regulatory networks (GRNs), they often fail to quantify time-resolved regulatory effects within specific temporal windows. Here, we present Time-varying Network Driver Estimation (TNDE), a computational framework quantifying dynamic gene driver effects from single-cell snapshot data under a linear Markov assumption. TNDE leverages a shared graph attention encoder to preserve the local topological structure of the data. Furthermore, by incorporating partial optimal transport, TNDE accounts for unmatched cells arising from proliferation or apoptosis, thereby enabling trajectory alignment in non-equilibrium processes. Benchmarking on simulated datasets demonstrates that TNDE outperforms existing baseline methods across diverse complex regulatory scenarios. Applied to mouse erythropoiesis data, TNDE identifies stage-specific driver genes, the functional relevance of which is corroborated by biological validation. TNDE offers an effective quantitative tool for dissecting dynamic regulatory mechanisms underlying complex biological processes.

Hong Qin, a computer science professor at the University of Tennessee at Chattanooga, was born in a town on the eastern coast of China not far from the birthplace of Confucius. The great Chinese philosopher once said, "Real knowledge is to know the extent of one's ignorance." Confucius was probably onto something when he said real knowledge is knowing your limits. Qin (pronounced "chin") works in a field, computational biology, that's so intricate that it helps to have an appreciation for the limits of the human brain. More and more, human researchers such as Qin are humbling themselves and allowing artificial intelligence models and supercomputers do the heavy lifting of scientific discovery.

Bengaluru: Researchers at Indian Institute of Technology (IIT)-Madras have developed a machine learning (ML) algorithm to identify personalised genes that have the potential to form and drive cancer in individuals. The model uses a'multiomic' approach, the combined study of intersectional studies that end with the suffix '-omics'. Details of the algorithm were published in a peer-reviewed paper in the journal Frontier in Genetics last month. The findings are expected to help in devising more personalised cancer therapies, contributing to the growing field of targeted therapy and immunotherapy trials. Called'Personalized Identification of driVer OGs and TSGs', or PIVOT, the model identifies personalised drivers of cancer genes and classifies them as either tumour suppressor genes (TSG) or oncogenes (OG) -- the two types of genes involved in cancer.

Causal Inference methods based on observational data are an alternative for applications where collecting the counterfactual data or realizing a more standard experiment is not possible. In this work, our goal is to combine several observational causal inference methods to learn new causes in applications where some causes are well known. We validate the proposed method on The Cancer Genome Atlas (TCGA) dataset to identify genes that potentially cause metastasis.

The single-molecule multiplex chromatin interaction data are generated by emerging 3D genome mapping technologies such as GAM, SPRITE, and ChIA-Drop. These datasets provide insights into high-dimensional chromatin organization, yet introduce new computational challenges. MIA-Sig is an algorithmic solution based on signal processing and information theory. The authors demonstrate its ability to de-noise the multiplex data, assess the statistical significance of chromatin complexes, and identify topological domains and frequent inter-domain contacts. Identifying personalized driver genes that lead to particular cancer initiation and progression of individual patient is one of the biggest challenges in precision medicine.