The human gut microbiome has emerged as a key player in health and disease, yet machine learning on microbiome data remains challenging due to its high dimensionality, sparsity, compositionality, and inter-study heterogeneity. Although classical and deep learning methods have demonstrated promise, they often require extensive labeled data, which is rarely available in microbiome research. In this thesis, we investigate whether meta-learning can address these challenges by enabling better generalization from small, heterogeneous microbiome datasets. Specifically, we benchmark Prototypical networks (Protonets), a metric-based, few-shot meta-learning algorithm, against strong classical baselines (Random Forests, XGBoost, and Multi-layer Perceptrons) for disease classification tasks across a selected number of gut microbiome studies. We introduce a unified benchmarking pipeline that standardizes preprocessing, dimensionality reduction, task construction, and evaluation across studies. A leave-one-study-out cross-validation strategy simulates realistic deployment scenarios where only a few labeled samples are available from a new cohort. Our experiments explore the impact of support set size and dimensionality reduction via principal component analysis. Results show that although Protonets offer a conceptually appealing approach for few-shot learning, they consistently underperform compared to Random Forests in classification accuracy. Statistical analyses confirm the significance of this performance gap, and embedding visualizations reveal limited class separation in the learned feature space. These findings suggest that, under the evaluated conditions, classical models like Random Forests remain the more robust choice for microbiome classification in low-data regimes. By offering a rigorous and reproducible evaluation, this work lays the foundation for further exploration of meta-learning in microbiome research and highlights both the potential and current limitations of learning to learn in this complex domain.

Metagenomic Next-Generation Sequencing (mNGS) presents a promising avenue to generate massive volume of sequence reads in a short period of time. This has opened opportunities for disease diagnosis based on individual variations and mutations by considering the microbiome profile of each patient. However, the effective use of this data requires the design of appropriate algorithms which can closely represent the metagenomic data in an accurate and condensed manner.

In this work, we acknowledged the efficiency of current approaches such as reference based methods and frequency encoding. However, we also recognized the limitations of current methods, such as limiting findings to pre-existing knowledge and inadequate representation of reads and metagenomic samples. Accordingly, we explored a natural
language embedding technique, called Doc2vec, as a potential embedding approach for metagenomic study and phenotype prediction.

We introduced some modifications in the original Doc2Vec architecture to remove a bottleneck in analysing long reads. This was done by replacing k-mer-level encoding with nucleotide-level representation. We used the embeddings obtained from this method as input to logistic classifier and ridge regression models. We compared the results with Kraken2 on colorectal cancer and type-2 diabetes classification, and for regression tasks on type-2 diabetes-related measures.

The results suggest a comparable performance between the proposed method and reference-based method for colorectal cancer classification. For type-2 diabetes dataset, reference-based method performs significantly better. In regression tasks to predict various metrics associated with type-2 diabetes, the proposed representation was comparable to reference-based method for some phenotypes, but lacked flexibility in others, indicating that the applicability of proposed approach strongly depends on the objective, dataset, and target phenotype.