Yeni Bir DNA Sınıflandırma Deneyi: Kolmogorov-Arnold Ağları ile İnsan Maymun Çiçeği DNA-Motifleri için Mekânsal Transkriptomik Analizi

Öz

Mekânsal Transkriptomik (ST), bir doku veya organizmanın farklı bölgelerindeki gen ifade modellerini anlamak için güçlü bir araç olarak ortaya çıkmıştır. Hastalık araştırmaları ve yeni tedavilerin geliştirilmesi için çok önemlidir. Bir doku lamının belirli, lokalize alanlarında gen ifadesinin ölçülmesine izin verir, ancak bunu sınırlı verimle yapar. Bununla birlikte, ST teknolojileri tarafından üretilen veriler karakteristik olarak gürültülü, yüksek boyutlu, seyrek ve çok modludur; histolojik görüntüler ve sayım matrisleri gibi unsurları kapsar. ST verilerini analiz etmek için genellikle geleneksel istatistiksel veya makine öğrenimi tekniklerine dayanan mevcut yöntemlerin, ölçek, çok modluluk ve uzamsal çözünürlük, hassasiyet ve gen kapsamı dahil olmak üzere uzamsal olarak çözülmüş verilerin doğal sınırlamaları gibi zorluklar nedeniyle birçok durumda yetersiz olduğu kanıtlanmıştır. Bu özel zorlukların üstesinden gelmek için araştırmacılar derin öğrenme tabanlı modellere yönelmiştir. Bu çalışmada, maymun çiçeği transkriptomik örneğinin bölgesel kökenini tahmin etmek için son teknoloji ürünü bir derin öğrenme modeli olan Kolmogorov-Arnold Ağlarını (KAN'lar) kullanarak transkriptomik analizine yeni bir yaklaşım sunuyoruz. KAN'ların karmaşık, doğrusal olmayan işlevleri öğrenme ve temsil etme yeteneğinden yararlanarak, gen ifadesinin karmaşık uzamsal modellerini ortaya çıkarmayı ve altta yatan biyolojik süreçler hakkında içgörü kazanmayı amaçlıyoruz. Çalışmanın analizi Amerika ve Asya olmak üzere iki farklı bölgeye odaklanmakta ve KAN tabanlı bir sınıflandırıcı kullanmaktadır. Sonuçlar, Amerika bölgesi için 0,45 hassasiyet ve 0,93 geri çağırma ile KAN'ların bu bağlamda umut verici performansını göstermektedir, bu da bu bölgeden örnekleri doğru bir şekilde tanımlama konusunda güçlü bir yeteneğe işaret etmektedir. Bulgular, DNA motiflerinden maymun çiçeğinin bölgesel transkriptomunun tahmin edilmesinin filogenetik analizler için görüntü tabanlı taramayı kolaylaştırabileceğini göstermektedir.

Anahtar Kelimeler

• Mekânsal Transkriptomik
• Kolmogorov-Arnold Ağları
• Derin Öğrenme
• Gen Örüntüleri
• Bölgesel Sınıflandırma

A Novel DNA Classification Experiment: Spatial Transcriptomics analysis for human Monkeypox DNA-Motifs with Kolmogorov–Arnold Networks

Abstract

Spatial Transcriptomics(ST) has emerged as a powerful tool for understanding gene expression patterns across different regions of a tissue or organism. It is crucial for disease research and developing new therapies. It allows for the measurement of gene expression across specific, localized areas of a tissue slide, though it does so with limited throughput. Yet, the data produced by ST technologies are characteristically noisy, high-dimensional, sparse, and multi-modal, encompassing elements like histological images and count matrices. Existing methods for analyzing ST data, which often rely on traditional statistical or machine learning techniques, have proven inadequate in many cases due to challenges like scale, multi-modality, and the inherent limitations of spatially-resolved data, including spatial resolution, sensitivity, and gene coverage. To address these specific challenges, researchers have turned to deep learning-based models. In this study, we present a novel approach to transcriptomics analysis using Kolmogorov-Arnold Networks (KANs), a state-of-the-art deep learning model to predict regional origin of monkeypox transcriptomic sample. By leveraging the ability of KANs to learn and represent complex, non-linear functions, we aim to uncover intricate spatial patterns of gene expression and gain insights into the underlying biological processes. Study’s analysis focuses on two distinct regions, America and Asia, and employs a KAN-based classifier. The results demonstrate the promising performance of KANs in this context, with a precision of 0.45 and a recall of 0.93 for the America region, indicating a strong ability to correctly identify samples from this region. Findings indicate that predicting the regional transcriptome of monkeypox from DNA motifs could facilitate image-based screening for phylogenetic analyses.

Keywords

• Deep Learning
• Gene Expression Patterns
• Regional Classification
• Spatial Transcriptomics
• Kolmogorov-Arnold Networks

Kaynakça

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