@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.} } @conference {cMas-Montserrat, title = {Generative Moment Matching Networks for Genotype Simulation}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

The generation of synthetic genomic sequences using neural networks has potential to overcome privacy and data sharing restrictions and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively asses the quality of the simulated sequences.

}, author = {Mas-Montserrat, Daniel and Perera, Maria and Barrab{\'e}s, M{\'\i}riam and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xBarrabes22, title = {Machine Learning for Genomic Sequence Processing}, year = {2022}, abstract = {

Introduction to Research, BSc Data Science and Engineering, Autumn 2021:

Predicting Dog Phenotypes from Genotypes

In this paper, we analyze dog genotypes {\textendash} positions of DNA sequences that often vary between different dogs {\textendash}in order to predict the corresponding phenotypes {\textendash} unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare its performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the results of all of these methods based on the number of input features used in the analy sis and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the full genomic sequences for our analysis.

MergeGenome. A Python-based Toolkit for Merging VCF files

A challenge of genomic studies is the lack of easy to access and properly formatted datasets. When having access to more than one dataset, it seems desirable to combine them. There is a lack of tools to duly merge genomic datasets without losing all non-matching features. To fill this gap, we present the MergeGenome toolkit, designed to integrate DNA sequences from two files in variant call format (VCF) while targeting data quality. MergeGenome is a robust pipeline of comprehensive steps to standardize nomenclature, remove ambiguities, correct flips, eliminate mismatches, select important features, and filter likely erroneous features (the latter with machine learning). MergeGenome is Python-based and relies on pre-existing software for manipulation and imputation of VCF data. We evaluate the result of merging two datasets with dog DNA sequences of dissimilar lengths and notice that genotype imputation with Beagle v5.1 usually fails for low-frequency alleles. Alternatively, we explore several multi-label machine learning classifiers. Although K-Nearest Neighbors achieves competitive results, none of the methods tried outperforms Beagle v5.1.

}, author = {Barrab{\'e}s, M{\'\i}riam}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

In this paper, we analyze dog genotypes - positions of DNA sequences that often vary between different dogs - in order to predict the corresponding phenotypes - unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We show that linear methods generally outperform or match non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the performance of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features available for our analysis, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the features.

}, doi = {https://doi.org/10.1101/2022.04.13.488108 }, author = {Bartusiak, Emily and Barrab{\'e}s, M{\'\i}riam and Aigerim Rymbe and J{\'u}lia Gimbernat and Cayetana L{\'o}pez and Lorenzo Barberis and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} }