@article {aBarrabes, title = {Advances in Biomedical Missing Data Imputation: A Survey}, journal = {IEEE Access}, year = {2024}, month = {12/2024}, abstract = {

Ensuring data quality in biomedical sciences is crucial for reliable research outcomes, particularly as precision medicine continues to gain prominence. Missing values compromise data quality and can make it difficult to perform data-based studies. The origins of missing values in biomedical datasets are diverse, including experimental errors, equipment malfunctions, and variations in data collection protocols tailored to individual patient conditions. To address the complex nature of missing values and the unique characteristics of biomedical data, a diverse spectrum of computational imputation techniques has emerged. These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. We outline the fundamental principles underlying each imputation technique and present a detailed analysis of their advantages and disadvantages, categorized by missing data patterns. To aid practitioners in method selection, we offer practical recommendations based on critical factors such as dataset size, data type, and missingness rate. By synthesizing insights from existing literature, we provide a holistic perspective on the effectiveness of various imputation methods under different biomedical contexts, thereby facilitating informed decision-making for researchers and practitioners in applying imputation techniques to biomedical data processing.

}, issn = {2169-3536}, doi = {10.1109/ACCESS.2024.3516506}, url = {http://hdl.handle.net/2117/422141}, author = {Barrab{\'e}s, M{\'\i}riam and Perera, Maria and Novelle, Victor and Moriano, Xavier and Xavier Gir{\'o}-i-Nieto and Mas-Montserrat, Daniel and Ioannidis, Alexander G.} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

TL;DR: We introduce a framework inspired in adversarial learning to detect and correct features originating a distribution shift between datasets.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods trying to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts outperforming current statistical and neural network-based techniques.

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xPerera22, title = {Ancestry-conditioned Generative Models for Genotyping}, year = {2022}, abstract = {

Local ancestry inference (LAI) identifies the ancestry of each segment of an individual{\textquoteright}s genome and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and personalized medicine to increase detection of genetic associations.\ 

New LAI techniques are appearing at a fast pace in both industry and academic research and large data-sets of human genomic sequences from the ancestries of interest are required to train those methods. Usually, those data-sets are protected by privacy regulations, are proprietary or accessible only when they come with restrictions due to its nature. An interesting way to overcome those difficulties is through the generation of data samples that could be similar enough to real sequences from ancestries of interest. A generalized model can be openly shared because there is no real individual information in there.\ 

Thus, we present a class-conditional Generative adversarial Model and a Conditional Generative Moment-Matching Network intended to generate new realistic genotypes of a desired ancestry. In addition, we present a privacy mechanism that extracts features from the real data to generate new realistic genotypes by using features.

}, author = {Perera, Maria}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} }