AbstractBackgroundMachine learning (ML) and artificial intelligence (AI) tools have revolutionized genetic disorder prediction from genomic data. In this respect, ML algorithms can discover relationships among widely spread genomic sequences which might be in relation to indicators of certain genetic conditions. LSTMs have the capability to handle bigger and more complicated datasets than what traditional statistical methods can support.

A prime use case is supervised learning – algorithms trained on labeled datasets consisting of the genetic data from individuals with and without known disorders (Image 3). These models are taught to anticipate the occurrence of genetic disorders in new, untagged genomic sequences based on learning features and patterns associated with these conditions.

This is why deep learning, a sub-area of ML work so well in this area. Deep learning models such convolutional and recurrent neural networks have the capability or retaining complex patterns in genomic data. For example, CNNs can understand the spatial arrangement of nucleotides while clarity with needles data being a sequence tends to make it more suitable for RNN.

These regions make up for interesting gene-sequences.

AI enables to find new genetic variants for diagnostics without any supervised learning, which would reveal unmet gene-disorder associations. Furthermore, AI-based models are able to pool different types of data (e.g., clinicodemographic and environmental factors) equally well- leading to higher prediction precision.

On the other hand, ML and AI combined advances our comprehension of genetic disorders exponentially leading to tailor-made treatments as well as early interventions.