Histone mark age of human tissues and cell types.

Aging is a complex and multifaceted process involving many epigenetic alterations. One key area of interest in aging research is the role of histone modifications, which can dynamically regulate gene expression. Here, we conducted a pan-tissue analysis of the dynamics of seven key histone modifications during human aging.

One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data.

Multimodal models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to disease diagnosis. Despite the importance of multimodal learning, existing efforts focus on vision-language applications, where the number of modalities rarely exceeds four (images, text, audio, video). However, data in healthcare domain, may include many more modalities like X-rays, PET scans, MRIs, genetic screening, genomic data, and clinical notes, creating a need for both efficient and accurate data integration.

scMultiNODE : Integrative Model for Multi-Modal Temporal Single-Cell Data.

Measuring single-cell genomic profiles at different timepoints enables our understanding of cell development. This understanding is more comprehensive when we perform an integrative analysis of multiple measurements (or modalities) across various developmental stages. However, obtaining such measurements from the same set of single cells is resource-intensive, restricting our ability to study them integratively.

Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation.

Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference; however, this information is not always available due to the limitations in sequencing depth.

scNODE : generative model for temporal single cell transcriptomic data prediction.

Summary: Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses.

scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.

Summary: Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data.

Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.

Background: Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.

Predicting A/B compartments from histone modifications using deep learning.

The three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization.

Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models.

Background: Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains.

Objective: This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.

A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods.

Hi-C is a widely used technique to study the 3D organization of the genome. Due to its high sequencing cost, most of the generated datasets are of a coarse resolution, which makes it impractical to study finer chromatin features such as Topologically Associating Domains (TADs) and chromatin loops. Multiple deep learning-based methods have recently been proposed to increase the resolution of these datasets by imputing Hi-C reads (typically called upscaling).

CellBiAge: Improved single-cell age classification using data binarization.

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise.

Memory-efficient semantic segmentation of large microscopy images using graph-based neural networks.

We present a graph neural network (GNN)-based framework applied to large-scale microscopy image segmentation tasks. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by the image size that can fit in the memory of computational hardware. In a GNN framework, large-scale images are converted into graphs using superpixels (regions of pixels with similar color/intensity values), allowing us to input information from the entire image into the model.

Chi3l1 Is a Modulator of Glioma Stem Cell States and a Therapeutic Target in Glioblastoma.

Unlabelled: Chitinase 3-like 1 (Chi3l1) is a secreted protein that is highly expressed in glioblastoma. Here, we show that Chi3l1 alters the state of glioma stem cells (GSC) to support tumor growth. Exposure of patient-derived GSCs to Chi3l1 reduced the frequency of CD133+SOX2+ cells and increased the CD44+Chi3l1+ cells.

Clinical capability of modern brain tumor segmentation models.

Purpose: State-of-the-art automated segmentation methods achieve exceptionally high performance on the Brain Tumor Segmentation (BraTS) challenge, a dataset of uniformly processed and standardized magnetic resonance generated images (MRIs) of gliomas. However, a reasonable concern is that these models may not fare well on clinical MRIs that do not belong to the specially curated BraTS dataset. Research using the previous generation of deep learning models indicates significant performance loss on cross-institutional predictions.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data.

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference.

SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments.

Multimodal attention-based deep learning for Alzheimer's disease diagnosis.

Objective: Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis.

Materials And Methods: We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data.

Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks.

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SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport.

Recent advances in sequencing technologies have allowed us to capture various aspects of the genome at single-cell resolution. However, with the exception of a few of co-assaying technologies, it is not possible to simultaneously apply different sequencing assays on the same single cell. In this scenario, computational integration of multi-omic measurements is crucial to enable joint analyses.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository.

Single-Cell Multiomics Integration by SCOT.

Although the availability of various sequencing technologies allows us to capture different genome properties at single-cell resolution, with the exception of a few co-assaying technologies, applying different sequencing assays on the same single cell is impossible. Single-cell alignment using optimal transport (SCOT) is an unsupervised algorithm that addresses this limitation by using optimal transport to align single-cell multiomics data. First, it preserves the local geometry by constructing a -nearest neighbor (-NN) graph for each data set (or domain) to capture the intra-domain distances.