\begin{algorithm}[t]
\caption{Constructing a pseudo-image from a whole genome sample for a given k-mer size $k$.}\label{alg:cap}
\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-occurrence frequency}
\State $r \gets \frac{e_{i,j}}{(\Vert e_{i,j} - e^{\prime}_{i,j} \Vert_2)}$ \Comment{Update co-occurrence}
\State $e_{i,j} = 2 \sqrt{max(r - 1, 1)} + (min(r-2,2) + 2)$ 
\State \textbf{return} $e_{i,j}$\Comment{Relative co-occurrence frequency}
\EndProcedure
\Procedure{NormalizeImage}{$I_r, \lambda_m$}
\State $N\gets\sum_{i=0}^{4^k}{\sum_{j=0}^{4^k}{I[i,j]}}$
\For{$i \in 4^k$}
\For{$j \in 4^k$}
\If{$I_r[i,j] > \lambda_m$}
\State$I_r[i,j]\gets 255*I[i,j]/N$
\Else
\State$I_r[i,j]\gets 0$
\EndIf
\EndFor
\EndFor
\State \textbf{return} $I_r$\Comment{Normalized Pseudo-Image}
\EndProcedure
\State $I_r \gets ones(4^k,4^k)$ \Comment{Initial pseudo-image}
\For{$ X_i \in \mathcal{X}_i $} \Comment{Iterate through each sequence}
\For{$ x_i, x_j \in X_i $} \Comment{Iterate through successive k-mers}
\State $I_r[i,j]\gets$\Call{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\EndFor
\EndFor
\State $I_r\gets$ \Call{NormalizeImage}{$I_r, \lambda_m$}
\State \textbf{return} $I_r$\Comment{Final pseudo-image}
\end{algorithmic}
\end{algorithm}

\begin{algorithm}
[t]
\caption{Constructing a pseudo-image from a whole genome sample for a given k-mer size $k$.}\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-occurrence frequency}
\State $r \gets \frac{e_{i,j}}{(\Vert e_{i,j} - e^{\prime}_{i,j} \Vert_2)}$ \Comment{Update co-occurrence}
\State $e_{i,j} = 2 \sqrt{max(r - 1, 1)} + (min(r-2,2) + 2)$
\State \textbf{return} $e_{i,j}$\Comment{Relative co-occurrence frequency}
\EndProcedure
\Procedure{NormalizeImage}{$I_r, \lambda_m$}
\State $N\gets\sum_{i=0}^{4^k}{\sum_{j=0}^{4^k}{I[i,j]}}$
\For{$i \in 4^k$}
\For{$j \in 4^k$}
\If{$I_r[i,j] > \lambda_m$}
\State$I_r[i,j]\gets 255*I[i,j]/N$
\Else
\State$I_r[i,j]\gets 0$
\EndIf
\EndFor
\EndFor
\State \textbf{return} $I_r$\Comment{Normalized Pseudo-Image}
\EndProcedure
\State $I_r \gets ones(4^k,4^k)$ \Comment{Initial pseudo-image}
\For{$ X_i \in \mathcal{X}_i $} \Comment{Iterate through each sequence}
\For{$ x_i, x_j \in X_i $} \Comment{Iterate through successive k-mers}
\State $I_r[i,j]\gets$\Call{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\EndFor
\EndFor
\State $I_r\gets$ \Call{NormalizeImage}{$I_r, \lambda_m$}
\State \textbf{return} $I_r$\Comment{Final pseudo-image}
\end{algorithmic}
\end{algorithm}
{
    "title": "tepi: taxonomy-aware embedding and pseudo-imaging for scarcely-labeled   zero-shot genome classification",
    "id": "2401.13219",
    "abstract": "a species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classification, and understanding genetic predispositions like drug resistance and virulence. however, the vast number of potential species poses significant challenges in developing a general-purpose whole genome classification tool. traditional bioinformatics tools have made notable progress but lack scalability and are computationally expensive. machine learning-based frameworks show promise but must address the issue of large classification vocabularies with long-tail distributions. in this study, we propose addressing this problem through zero-shot learning using tepi, taxonomy-aware embedding and pseudo-imaging. we represent each genome as pseudo-images and map them to a taxonomy-aware embedding space for reasoning and classification. this embedding space captures compositional and phylogenetic relationships of species, enabling predictions in extensive search spaces. we evaluate tepi using two rigorous zero-shot settings and demonstrate its generalization capabilities qualitatively on curated, large-scale, publicly sourced data.",
    "categories": "q-bio.gn cs.ai cs.lg",
    "doi": "",
    "created": "2024-01-23",
    "updated": "",
    "authors": [
        "sathyanarayanan aakur",
        "vishalini r. laguduva",
        "priyadharsini ramamurthy",
        "akhilesh ramachandran"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2401.13219"
}
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