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Y.I. Tepeli

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ELISL: early-late integrated synthetic lethality prediction in cancer

Journal article - Y.I. Tepeli, C.F. Seale, Joana Gonçalves

Motivation Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, ...

Overcoming Selection Bias in Synthetic Lethality Prediction

Journal article - C.F. Seale, C.F. Seale, Y.I. Tepeli, Joana Gonçalves

Motivation Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data. Results We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples. Availability and implementation https://github.com/joanagoncalveslab/sbsl Supplementary information Supplementary data are available at Bioinformatics online.@en

Contributed

7 records found

Assessing Machine Learning Robustness to Sample Selection Bias

Evaluating the effectiveness of semi-supervised learning techniques

Bachelor thesis - V.A.A. Biharie, Joana Gonçalves, Y.I. Tepeli

This paper tackles the problem of sample selection bias in machine learning, where the assumption of train and test sets being drawn from the same distribution is often violated. Existing solutions in domain adaptation, such as semi-supervised learning techniques, aim to correct ...

The effectiveness of subspace mapping techniques adapted to unlabeled samples from a global domain in mitigating sample selection bias

Bachelor thesis - T.F.R. van Hoorn, Joana Gonçalves, Y.I. Tepeli, Julián Urbano

Sample selection bias occurs when the selected samples in a subset of the original data set follow a different distribution than the samples from the original data set. This type of bias in the training set could result in a classifier being unable to predict samples from a testi ...

How effective are minimax methods in mitigating sample selection bias?

Bachelor thesis - Z. Khan, Y.I. Tepeli, Joana Gonçalves

Sample selection bias is a well-known problem in machine learning, where the source and target data distributions differ, leading to biased predictions and difficulties in generalization. This bias presents significant challenges for modern machine learning algorithms. To tackle ...

Evaluating the Effectiveness of Importance Weighting Techniques in Mitigating Sample Selection Bias

Bachelor thesis - A.C. TOCIU, Joana Gonçalves, Y.I. Tepeli, Julián Urbano

Importance weighting is a class of domain adaptation techniques for machine learning, which aims to correct the discrepancy in distribution between the train and test datasets, often caused by sample selection bias. In doing so, it frequently uses unlabeled data from the test set ...

Generalizability of Deep Domain Adaptation in case of Sample Selection Bias

Bachelor thesis - E.A. Witting, Y.I. Tepeli, Joana Gonçalves, Julián Urbano

Domain adaptation allows machine learning models to perform well in a domain that is different from the available train data. This non-trivial task is approached in many ways and often relies on assumptions about the source (train) and target (test) domains. Unsupervised domain a ...

Mitigating selection bias in synthetic lethality prediction using metric learning

Master thesis - M.J. de Wolf, Joana Gonçalves, P.K. Murukannaiah, Y.I. Tepeli

Synthetic lethality (SL) is a relationship between two genes, exploited for targeted anti-cancer therapy, whereby functional loss of both genes induces cell death, but the functional loss of either gene alone is non-lethal. Computational prediction of SL gene pairs is sought afte ...

Integrated learning of mutational signatures and prediction of DNA repair deficiencies

Master thesis - A.C.H. Goossens, Joana Gonçalves, Y.I. Tepeli, M.J.T. Reinders, J. Pothof, M. Khosla

Motivation: Many tumors show deficiencies in DNA damage repair. These deficiencies can play a role in the disease, but also expose vulnerabilities with therapeutic potential. Targeted treatments exploit specific repair deficiencies, for instance based on synthetic lethality. To decide which patients could benefit from such therapies requires the ability to determine the repair deficiency status of a tumor. It has been suggested that mutational signatures could be better predictors of DNA repair deficiency than loss of function in select genes. However, current models for prediction of repair deficiency rely on mutational signatures extracted using unsupervised learning techniques. As a result, the signatures are not optimized to discriminate between repair deficiency status or pathway. We argue that the supervised learning of mutational signatures guided by repair deficiency status could enable the identification of signatures that are predictive of repair deficiency, and capture underlying mechanisms of DNA repair. Results: We propose S-NMF, a supervised non-negative matrix factorization method, which jointly optimizes two objectives: (1) learning of signatures shared across tumor samples using NMF, and (2) learning of signatures predictive of repair deficiency using logistic regression. We apply S-NMF to mutation profiles of human induced pluripotent cell lines carrying knockouts of genes involved in three DNA repair pathways: homologous recombination, base excision repair, and mismatch repair. We show that S-NMF achieves high prediction accuracy (0.971) and learns signatures that better distinguish the repair deficiency of a sample. Signatures extracted by S-NMF are similar to cancer-related signatures associated with the same repair deficiency. Additionally, S-NMF can capture signatures of deficiencies affecting distinct subpathways within a main repair pathway (e.g. OGG1 and UNG mechanisms in base excision repair).