In Human brain mapping

Park Chang-Hyun, Shin Na-Young, Nam Yoonho, Yoon Uicheul, Ahn Kookjin, Lee Seung-Koo

2023-Mar-17

age estimation, machine learning, normal aging, perivascular space

In Journal of the National Cancer Institute

Reis-Filho Jorge S, Kather Jakob Nikolas

2023-Mar-17

Artificial intelligence, deep learning, machine learning, oncology, pathology

In Bioinformatics (Oxford, England)

MOTIVATION : The integration of single-cell multi-omics data can uncover the underlying regulatory basis of diverse cell types and states. However, contemporary methods disregard the omics individuality, and the high noise, sparsity, and heterogeneity of single-cell data also impact the fusion effect. Furthermore, available single-cell clustering methods only focus on the cell type clustering, which can not mine the alternative clustering to comprehensively analyze cells.

RESULTS : We propose a single-cell data fusion based multiple clustering (scMCs) approach that can jointly model single-cell transcriptomics and epigenetic data, and explore multiple different clusterings. scMCs first mines the omics-specific and cross-omics consistent representations, then fuses them into a co-embedding representation, which can dissect cellular heterogeneity and impute data. To discover the potential alternative clustering embedded in multi-omics, scMCs projects the co-embedding representation into different salient subspaces. Meanwhile, it reduces the redundancy between subspaces to enhance the diversity of alternative clusterings and optimizes the cluster centers in each subspace to boost the quality of corresponding clustering. Unlike single clustering, these alternative clusterings provide additional perspectives for understanding complex genetic information such as cell types and states. Experimental results show that scMCs can effectively identify subcellular types, impute dropout events, and uncover diverse cell characteristics by giving different but meaningful clusterings.

AVAILABILITY : The code is available at www.sdu-idea.cn/codes.php?name=scMCs.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Ren Liangrui, Wang Jun, Li Zhao, Li Qingzhong, Yu Guoxian

2023-Mar-17

In Bioinformatics (Oxford, England)

MOTIVATION : Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optical genome mapping utilizing Deep Learning is presented, termed DeepOM. Utilization of a Convolutional Neural Network (CNN), trained on simulated images of labeled DNA molecules, improves the success rate in alignment of DNA images to genomic references.

RESULTS : The method is evaluated on acquired images of human DNA molecules stretched in nano-channels. The accuracy of the method is benchmarked against state-of-the-art commercial software Bionano Solve. The results show a significant advantage in alignment success rate for molecules shorter than 50 kb. DeepOM improves yield, sensitivity and throughput of optical genome mapping experiments in applications of human genomics and microbiology.

AVAILABILITY AND IMPLEMENTATION : The source code for the presented method is publicly available at https://github.com/yevgenin/DeepOM.

SUPPLEMENTARY INFORMATION : Supplementary information is available at Bioinformatics online.

Nogin Yevgeni, Detinis Zur Tahir, Margalit Sapir, Barzilai Ilana, Alalouf Onit, Ebenstein Yuval, Shechtman Yoav

2023-Mar-17

In Medical physics ; h5-index 59.0

BACKGROUND : Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.

PURPOSE : We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.

METHODS : A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics.

RESULTS : Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r² ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r² was 0.899 for both FRC and TLC output spaces, and for single-input models r² was 0.803 and 0.862, respectively.

CONCLUSIONS : Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

Gerard Sarah E, Chaudhary Muhammad Fa, Herrmann Jacob, Christensen Gary E, Estépar Raúl San José, Reinhardt Joseph M, Hoffman Eric A

2023-Mar-17

computed tomography, deep learning, pulmonary

In Bioengineering & translational medicine

Huang Xi, Liu Bo, Guo Shenghan, Guo Weihong, Liao Ke, Hu Guoku, Shi Wen, Kuss Mitchell, Duryee Michael J, Anderson Daniel R, Lu Yongfeng, Duan Bin

2023-Mar

coronary artery disease, diagnostics, machine learning, small extracellular vesicles, spectrogram