Publications

Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments

Dongjoon Lim

Changhyun Baek

2024-01-26

iScience (publié)

MiRGraph: A transformer-based feature learning approach to identify microRNA-target interactions by integrating heterogeneous graph network and sequence information

Pei Liu

Ying Liu

Jiawei Luo

Yue Li

MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression by targeting specific mRNAs. They can function as both tumor sup… (voir plus)pressors and oncogenes depending on the specific miRNA and its target genes. Detecting miRNA-target interactions (MTIs) is critical for unraveling the complex mechanisms of gene regulation and identifying therapeutic targets and diagnostic markers. There is currently a lack of MTIs prediction method that simultaneously performs feature learning on heterogeneous graph network and sequence information. To improve the prediction performance of MTIs, we present a novel transformer-based multi-view feature learning method, named MiRGraph. It consists of two main modules for learning the sequence and heterogeneous graph network, respectively. For learning the sequence-based feaature embedding, we utilize the mature miRNA sequence and the complete 3’UTR sequence of the target mRNAs to encode sequence features. Specifically, a transformer-based CNN (TransCNN) module is designed for miRNAs and genes respectively to extract their personalized sequence features. For learning the network-based feature embedding, we utilize a heterogeneous graph transformer (HGT) module to extract the relational and structural information in a heterogeneous graph consisting of miRNA-miRNA, gene-gene and miRNA-target interactions. We learn the TransCNN and HGT modules end-to-end by utilizing a feedforward network, which takes the combined embedded features of the miRNA-gene pair to predict MTIs. Comparisons with other existing MTIs prediction methods illustrates the superiority of MiRGraph under standard criteria. In a case study on breast cancer, we identified plausible target genes of an oncomir hsa-MiR-122-5p and plausible miRNAs that regulate the oncogene BRCA1.

2024-01-26

bioRxiv (prépublication)

doi.org

Human-Centered AI

Catherine Régis

Jean-Louis Denis

Maria Luciana Axente

Atsuo Kishimoto

2024-01-25

(publié)

doi.org

Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints

Balamurali Murugesan

Sukesh Adiga Vasudeva

Bingyuan Liu

Hervé Lombaert

Ismail Ben Ayed

Jose Dolz

Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly… (voir plus) in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has resulted in substantial progress. Nevertheless, these approaches are strongly inspired by the advancements in classification tasks, and thus their uncertainty is usually modeled by leveraging the information of individual pixels, disregarding the local structure of the object of interest. Indeed, only the recent Spatially Varying Label Smoothing (SVLS) approach considers pixel spatial relationships across classes, by softening the pixel label assignments with a discrete spatial Gaussian kernel. In this work, we first present a constrained optimization perspective of SVLS and demonstrate that it enforces an implicit constraint on soft class proportions of surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism to balance the contribution of the constraint with the primary objective, potentially hindering the optimization process. Based on these observations, we propose NACL (Neighbor Aware CaLibration), a principled and simple solution based on equality constraints on the logit values, which enables to control explicitly both the enforced constraint and the weight of the penalty, offering more flexibility. Comprehensive experiments on a wide variety of well-known segmentation benchmarks demonstrate the superior calibration performance of the proposed approach, without affecting its discriminative power. Furthermore, ablation studies empirically show the model agnostic nature of our approach, which can be used to train a wide span of deep segmentation networks.

2024-01-25

ArXiv (prépublication)

doi.org

arxiv.org

Beyond Predictive Algorithms in Child Welfare

Erina Seh-Young Moon

Erin Moon

Devansh Saxena

Tegan Maharaj

Shion Guha

2024-01-23

graphicsinterface.org/Graphics_Interface/2024/Conference (publié)

doi.org

openreview.net

Connectome-based reservoir computing with the conn2res toolbox

Laura E. Suárez

Agoston Mihalik

Filip Milisav

Kenji Marshall

Mingze Li

Petra E. Vértes

Guillaume Lajoie

Bratislav Mišić

2024-01-22

Nature Communications (publié)

doi.org

RapidBrachyTG43: A Geant4‐based TG‐43 parameter and dose calculation module for brachytherapy dosimetry

Jonathan Kalinowski

Shirin A. Enger

2024-01-22

Medical Physics (publié)

doi.org

Transnational conservation to anticipate future plant shifts in Europe

Yohann Chauvier-Mendes

Laura J. Pollock

Peter H. Verburg

Dirk N. Karger

Loïc Pellissier

Sébastien Lavergne

Niklaus E. Zimmermann

Wilfried Thuiller

2024-01-22

Nature Ecology & Evolution (publié)

doi.org

Gaining Biological Insights through Supervised Data Visualization

Jake S. Rhodes

Adrien Aumon

Sacha Morin

Marc Girard

Catherine Larochelle

Boaz Lahav

Elsa Brunet-Ratnasingham

Amélie Pagliuzza

Lorie Marchitto

Wei Zhang

Adele Cutler

F. Grand'Maison

Anhong Zhou

Andrés Finzi

Nicolas Chomont

Daniel E. Kaufmann

Stephanie Zandee

Alexandre Prat

Guy Wolf

Kevin R. Moon

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

2024-01-21

bioRxiv (prépublication)

doi.org

EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions

Dimitrios Sinodinos

Narges Armanfard

2024-01-20

ArXiv (prépublication)

doi.org

arxiv.org

PhotoBot: Reference-Guided Interactive Photography via Natural Language

Oliver Limoyo

Jimmy Li

Dmitriy Rivkin

Jonathan Kelly

Gregory Dudek

We introduce PhotoBot, a framework for fully automated photo acquisition based on an interplay between high-level human language guidance an… (voir plus)d a robot photographer. We propose to communicate photography suggestions to the user via reference images that are selected from a curated gallery. We leverage a visual language model (VLM) and an object detector to characterize the reference images via textual descriptions and then use a large language model (LLM) to retrieve relevant reference images based on a user's language query through text-based reasoning. To correspond the reference image and the observed scene, we exploit pre-trained features from a vision transformer capable of capturing semantic similarity across marked appearance variations. Using these features, we compute pose adjustments for an RGB-D camera by solving a perspective-n-point (PnP) problem. We demonstrate our approach using a manipulator equipped with a wrist camera. Our user studies show that photos taken by PhotoBot are often more aesthetically pleasing than those taken by users themselves, as measured by human feedback. We also show that PhotoBot can generalize to other reference sources such as paintings.

2024-01-19

ArXiv (prépublication)

doi.org

arxiv.org

Deployable Reinforcement Learning with Variable Control Rate

Dong Wang

Giovanni Beltrame

2024-01-17

ArXiv (prépublication)

doi.org

arxiv.org