The use of imaging and genetic data for biomarker detection and disease diagnosis can deepen the understanding of disease pathogenesis and assist in clinical diagnosis. However, current methods face two major challenges: 1) the significant heterogeneity between multimodal data hampers modality fusion and 2) effectively exploring consistency and variability information from similar diseases for enhancing model performance is difficult. In this paper, we propose a novel unified framework, termed dual adaptive disentangled representation learning (DADRL), to simultaneously achieve disease-shared and disease-specific biomarker detection as well as disease diagnosis. Our DADRL comprises three components: 1) a biology information constraints-based modality fusion strategy is applied to adaptively explore inter- and intra-modal correlations, thereby effectively fusing multimodal data; 2) a unified framework that integrates modality fusion and disease diagnosis is proposed to mine disease-related information for simultaneously accomplishing disease-related biomarker detection and disease diagnosis; and 3) disentangled representation learning and several adaptive metric constraints are incorporated into the unified framework to adaptively separate disease-specific information from disease-shared feature representations for effectively identifying disease-shared and disease-specific biomarkers, thereby deepening the understanding of disease pathogenesis. Extensive experiments on multiple real datasets and simulated data demonstrate that our method significantly improves performance of biomarker detection and disease diagnosis.

Large Language Models (LLMs) have showcased remarkable performance across a range of tasks but are hindered by their massive parameter sizes, which impose significant computational and storage demands. Pruning has emerged as an effective solution to reduce model size, but traditional methods often involve inefficient retraining or rely on heuristic-based one-shot approaches that lack theoretical guarantees. In this paper, we reformulate the pruning problem as an $\ell_0$-penalized optimization problem and propose a monotone accelerated Iterative Hard Thresholding (mAIHT) method. Our approach combines solid theoretical foundations with practical effectiveness, offering a detailed theoretical analysis that covers convergence, convergence rates, and risk upper bounds. Through extensive experiments, we demonstrate that mAIHT outperforms state-of-the-art pruning techniques by effectively pruning the LLaMA-7B model across various evaluation metrics.

MOTIVATION: Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues. RESULTS: We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION: The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.

Multinomial logistic regression is a useful model for predicting the probabilities of multiclass outcomes. Because of the complexity and high dimensionality of some data, it is challenging to fit a valid model with high accuracy and interpretability. We propose a novel sparse reduced-rank multinomial logistic regression model to jointly select variables and reduce the dimension via a nonconvex row constraint. We develop a block-wise iterative algorithm with a majorizing surrogate function to efficiently solve the optimization problem. From an algorithmic aspect, we show that the output estimator enjoys consistency in estimation and sparsity recovery even in a high-dimensional setting. The finite sample performance of the proposed method is investigated via simulation studies and two real image data sets. The results show that our proposal has competitive performance in both estimation accuracy and computation time. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71991474, 12171449, 11801540, and 12071494] and the Natural Science Foundation of Anhui Province [Grant BJ2040170017]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0132 .

MOTIVATION: The association analysis between genetic variants and imaging phenotypes must be carried out to understand the inherited neuropsychiatric disorders via imaging genetic studies. Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics. RESULTS: To address these challenges in both statistics and computation, we propose a novel co-sparse reduced-rank regression model that identifies complex correlations in a dimensional reduction manner. We developed an iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty. The simulation studies showed that our method achieved accurate and stable performance in parameter estimation and variable selection. In real application, the proposed approach successfully detected several novel Alzheimer's disease-related genetic variants and regions of interest, which indicate that our method may be a valuable statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION: The R package csrrr, and the code for experiments in this article is available in Github: https://github.com/hailongba/csrrr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. RESULTS: We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. AVAILABILITY AND IMPLEMENTATION: CSMD is freely available at https://github.com/liuyu8721/csmd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships. RESULTS: To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI). AVAILABILITY AND IMPLEMENTATION: Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.