High-dimensional genomic datasets contain complex patterns shaped by substantial biological noise, which pose major challenges for predictive modeling in genetics and breeding. Residual neural networks (ResNets) provide a powerful framework for capturing nonlinear genomic effects, but often overfit in settings where marker numbers greatly exceed sample sizes. As a solution, a range of regularization methods have been proposed. One promising approach relies on the proximal mapping technique, which is computationally efficient since it can be directly incorporated into the optimization algorithm. However, the performance of ResNets with various convex or non-convex proximal regularizers remains under-explored on high-dimensional data. In this study, we propose an extended stochastic adaptive proximal gradient ResNet method that can handle both convex and non-convex regularizers that range from $L_{0}$ to $L_{\infty }$ and give more analysis of the convergence guarantee for the convex and non-convex regularizers. Moreover, we evaluate the prediction performance in a supervised regression setting on four real high-dimensional genomic datasets from mice, pig, wheat, and loblolly pine. For comparison, we also implement and evaluate traditional sparse linear proximal methods with the same regularizers, as well as LightGBM. Experimental results demonstrate that an 18-layer ResNet with $L_{\frac{1}{2}}$ regularization outperforms other configurations on both mice and pig datasets. For the wheat and loblolly pine data, the 15-layer ResNet $L_{\frac{1}{2}}$ configuration achieves the lowest test mean squared errors and the highest distance correlation (dCor). These findings highlight the effectiveness of the regularized adaptive proximal gradient ResNet method and its potential for prediction tasks on high-dimensional genomic data.

Traditional quantitative genetics rely on known pedigrees for estimating polygenic breeding values and variance components. With the advent of high-throughput sequencing, estimation of empirical relationships between individuals have become feasible. The single-step genomic BLUP procedure integrates polygenic and genomic information but requires intricate pedigree connections, leading to computationally demanding matrix operations. Here, we propose DYpREG, a flexible proximal operator multi-objective regularization method. DYpREG is a splitting algorithm that accommodates two distinct loss functions and a regularization function that can be chosen as LASSO, ridge regression (RR) or elastic net (EN). Notably, it incorporates pre-calculated breeding values without the need for extensive relationship matrix computations. Evaluated on two traits with different heritabilities from mice and pig, DYpREG with RR regularizer showed the best predictive performance compared to DYpREG with LASSO and EN regularizers as well as to all methods lacking polygenic information. The mean squared error (MSE) reduction was notable for both mice traits (1.5% and 24.77%) and both pig traits (2.8% and 2.1%). Comparative analyses with Bayesian reproducing kernel Hilbert space (RKHS) and spike-and-slab (BayesC) regression also revealed consistent improvements. Hence, DYpREG effectively integrates polygenic information, enhancing prediction accuracy in genomic evaluations.

BACKGROUND: The recent development of high-throughput sequencing techniques provide massive data that can be used in genome-wide prediction (GWP). Although GWP is effective on its own, the incorporation of traditional polygenic pedigree information into GWP has been shown to further improve prediction accuracy. However, most of the methods developed in this field require that individuals with genomic information can be connected to the polygenic pedigree within a standard linear mixed model framework that involves calculation of computationally demanding matrix inverses of the combined pedigrees. The extension of this integrated approach to more flexible machine learning methods has been slow. METHODS: This study aims to enhance genomic prediction by implementing gated residual variable selection neural networks (GRVSNN) for multi-task genomic prediction. By integrating low-rank information from pedigree-based relationship matrices with genomic markers, we seek to improve predictive accuracy and interpretability compared to conventional regression and deep learning (DL) models. The prediction properties of the GRVSNN model are evaluated on several real-world datasets, including loblolly pine, mouse and pig. RESULTS: The experimental results demonstrate that the GRVSNN model outperforms traditional tabular genomic prediction models, including Bayesian regression methods and LassoNet. Using genomic and pedigree information, GRVSNN achieves a lower mean squared error (MSE), and higher Pearson (r) and distance (dCor) correlation between predicted and true phenotypic values in the test data. Moreover, GRVSNN selects fewer genetic markers and pedigree loadings which improves interpretability. CONCLUSION: The suggested GRVSNN framework provides a novel and computationally effective approach to improve genomic prediction accuracy by integrating information from traditional pedigrees with genomic data. The model's ability to conduct multi-task predictions underscores its potential to enhance selection processes in agricultural species and predict multiple diseases in precision medicine.

PURPOSE: More accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets. METHODS: The study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods. RESULTS: Extensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency. CONCLUSION: Through series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Natural type selection was originally proposed by Zamir and Rose for universal single description coding. In this paper, we generalize this principle to universal multiple description coding (MDC). Two schemes based on random codebooks are proposed: one is of fixed distortion and the other is of fixed weight. Their operational sum-rate-distortion functions are derived, which coincide with the EGC (El Gamal-Cover) sum-rate bound if the parameters of the schemes are optimized. It is also shown that in both schemes the joint type of reconstruction codewords can be used to improve the rate-distortion (R-D) performance. Based on our theoretical results, a practical universal scheme is proposed by leveraging the MDC methods based on low-density generator matrix (LDGM) codes. The performance of this scheme is compared experimentally with the EGC bound, which shows its effectiveness.

Recently, Østergaard and Zamir revealed the connection between multiple description coding and delta-sigma quantization (DSQ). The principle has been applied to image coding, with main focus on the framework where each block is processed separately. In this brief, we propose a two-channel multiple description image coding scheme that performs inter-block processing. The source image is first rearranged into a block sequence. Then, vector DSQ is performed with a bank of noise-shaping filters. Their coefficients as well as the quantization steps are chosen by a rate-distortion optimization algorithm. A post-processing algorithm is proposed for side decoding. Experiment results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality.

In this paper, we generalize the concept of natural type selection, initially proposed by R.Zamir et.al., to multiple description coding. We prove results that are parallel to those of adaptive single description coding. A universal multiple description coding scheme is then proposed. The scheme codes a source vector at a time, and updates its codebooks based on the joint type of the codewords reconstructed previously. Based on our theoretical results, it can be shown that the rate-distortion performance of the proposed scheme gradually improves as coding proceeds.

In this paper, we present a new method to recognize object class based on local appearance features and context information. At first, local descriptors of object class appearance are clustered, then part classifiers are trained to select the most distinctive image patches and visual context information around them are extracted to keep the robustness to object occlusion and background clutter. Finally general probabilistic models are built to implement image classification by integrating the context information with local scale-invariant appearance characteristics. Compared with previous work, we obtain a better classification with limited and unnormalized training samples. Experiment results show that the proposed method can outperform other previous methods even under large scale object classes, therefore the significance of appearance-based discriminative part classifiers is demonstrated and confirmed.

In this paper, we are to solve the drift problem of multiple description video coding on packet loss channels by using state-of-the-art distributed techniques. We first present an asymptotically optimal code design of multiple descriptions in the Wyner-Ziv (MDWZ) setting. Then we propose a distributed multiple description video coding (DMDVC) scheme, which performs MDWZ coding on each nonintra coded frame. Instead of the prediction loops used in traditional multiple description video coding, Slepian-Wolf based coding is used to exploit interframe correlations. A bitplane extraction scheme is proposed to improve the balance between two descriptions, so that side informations can be interchanged between the side decoders of DMDVC with negligible quality degradation, which is crucial to robust transmission over packet loss channels. Experiment results demonstrate the robustness of our scheme, especially at high packet loss rates.

In this paper, we propose two backward adaptive coding algorithms based on a recently invented pre/post-filtered DPCM (Differential Pulse-Coded Modulation) codec. The pre/post-filters and the predictor are adapted jointly. Source statistics are assumed unknown a priori. One of the algorithms is based on power spectrum estimation, and the other gradient descent. Some properties of the algorithms are analyzed. Experiment results show that both algorithms achieve near-optimal rate-distortion performance, significantly outperforming the adaptive DPCM without adaptive pre/post-filtering.

We propose a general N-channel MDC (Multiple Description Coding) framework which can integrate the advantages of various low-dimensional MDC schemes. Given the operational rate-distortion functions of low-dimensional codecs, we show how to optimize the proposed MDC framework. We prove that the optimization problem can be reduced to a combinatorial problem which in certain cases admits solutions. We then apply the proposed optimization algorithm to multiple description image coding. Experiment results show the effectiveness of our approach.

One major task in multiple description video coding is to prevent drift on packet loss channels, where transmission errors occur in each description. We propose a distributed multiple description video coding (DMDVC) scheme excluding any prediction loops. The new codec suffers from no drift problem. In the two-channel mode of symmetry side informations (SI), one side decoder can use the SI of the other without any decoding quality degradation. Thereby, DMDVC achieves high robustness on packet loss channels.