The core function of DiMA is to quantify how much a virus or protein sequence varies over time or across different species.
: In AI, the Dynamic Intra-modality Attention (DIMA) module generates conditioning gates to help models focus on relevant audio or visual segments in video data.
: Measures the uncertainty or "randomness" at specific positions in a sequence.
: The identifier for the protein or virus being analyzed.
When you download the results from the DiMA tool, the output (often in text or JSON format) includes several specific data points:
: A count of the different sequence variations found at that specific position.
: Users can compare diversity patterns within a single protein or across entire proteomes. 📊 Data Facets in dima.txt/JSON
: The frequency of each specific k-mer within the dataset.