Biology subjects

Woicik, A.

Publications and source records attributed to Woicik, A..

4 recordsLinked to original sources

bioRxiv2023

Enhancing Hi-C contact matrices for loop detection with Capricorn, a multi-view diffusion model

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. On the other hand, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops, and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17.8% in mean squared error and 22.9% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 15.7% relative to existing methods. We further show that our multi-view idea can also be used to improve several existing methods, Hi-CARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

bioinformatics↗

bioRxiv2023

Gemini: Memory-efficient integration of hundreds of gene networks with high-order pooling

MotivationThe exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. ResultsTo address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 14% improvement in micro-AUPRC, and 71% improvement in macro-AURPC for protein function prediction by integrating hundreds of networks from BioGRID, and that Geminis performance significantly improves when more networks are added to the input network collection, while the comparison approachs performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks, and can be used to massively integrate and analyze networks in other domains. AvailabilityGemini can be accessed at: https://github.com/MinxZ/Gemini. Contactaddiewc@cs.washington.edu, swang@cs.washington.edu

bioinformatics↗

bioRxiv2022

Sagittarius: Extrapolating Heterogeneous Time-Series Gene Expression Data

Understanding the temporal dynamics of gene expression is crucial for developmental biology, tumor biology, and biogerontology. However, some timepoints remain challenging to measure in the lab, particularly during very early or very late stages of a biological process. Here we propose Sagittarius, a transformer-based model that can accurately simulate gene expression profiles at timepoints outside of the range of times measured in the lab. The key idea behind Sagittarius is to learn a shared reference space for time series measurements, thereby explicitly modeling unaligned timepoints and conditional batch effects between time series, and making the model widely applicable to diverse biological settings. We show Sagittariuss promising performance when extrapolating mammalian developmental gene expression, simulating drug-induced expression at unmeasured dose and treatment times, and augmenting datasets to accurately predict drug sensitivity. We also used Sagittarius to extrapolate mutation profiles for early-stage cancer patients, which enabled us to discover a gene set connected to the Hedgehog signaling pathway that may be related to tumorigenesis in sarcoma patients, including PTCH1, ARID2, and MYCBP2. By augmenting experimental temporal datasets with crucial but difficult-to-measure extrapolated datapoints, Sagittarius enables deeper insights into the temporal dynamics of heterogeneous transcriptomic processes and can be broadly applied to biological time series extrapolation.

genomics↗

bioRxiv2022

Pisces: A combo-wise contrastive learning approach to synergistic drug combination prediction

Drug combination therapy is promising for cancer treatment through simultaneously reducing resistance and improving efficacy. Machine learning approaches to drug combination response prediction can prioritize experiments and discover new combinations, but require lots of training data in order to fit the nonlinearity of synergistic effect. Here, we propose Pisces, a novel machine learning approach for drug combination synergy prediction. The key idea of Pisces is to augment the sparse drug combination dataset by creating multiple views for each drug combination based on its different modalities. We combined eight different modalities of a single drug to create 64 augmented views for a pair of drugs, effectively expanding the size of the original data 64 times. Pisces obtained state-of-the-art results on cell-line-based drug synergy prediction, xenograft-based drug synergy prediction, and drug-drug interaction prediction. By interpreting Piscess predictions using a genetic interaction network, we further identified a breast cancer drug-sensitive pathway from BRCA cell lines in GDSC. We validated this pathway on an independent TCGA-BRCA tumor dataset and found that patients with this pathway activated had substantially longer survival time. Collectively, Pisces effectively predicts drug synergy and drug-drug interactions through augmenting the original dataset 64 times, and can be broadly applied to various biological applications that involve a pair of drugs.

molecular biology↗