Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn
a specific task from the data and aims to develop computer algorithms that improve with …

More reliable and cheaper sequencing technologies have revealed the vast mutational
landscapes characteristic of many phenotypes. The analysis of such genetic variants has led …

Understanding genetic events that lead to cancer initiation and progression remains one of
the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver …

The integrator complex has been recently identified as a key regulator of RNA Polymerase II-
mediated transcription, with many functions including the processing of small nuclear RNAs …

Three-dimensional protein structural data at the molecular level are pivotal for successful
precision medicine. Such data are crucial not only for discovering drugs that act to block the …

Motivation Functional somatic mutations within coding amino acid sequences confer growth
advantage in pathogenic process. Most existing methods for identifying cancer-related …

Mechanisms of lung squamous cell carcinoma (LSCC) development are poorly understood.
Here, we report that JNK1/2 activities attenuate Lkb1-deficiency-driven LSCC initiation and …

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and
intellectual disabilities are heterogeneous conditions in which clinical genetic testing can …

Cancer mutations that are recurrently observed among patients are known as hotspots.
Hotspots are highly relevant because they are, presumably, likely functional. Known …

Somatic mutations often occur at high relapse sites in protein sequences, which indicates
that the location clustering of somatic missense mutations can be used to identify driving …