BACKGROUND: In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal and joint models, are proposed to identify important interaction factors. Most existing approaches for identifying G-E interactions are limited owing to the lack of robustness to outliers/contamination in response and predictor data. In particular, right-censored survival outcomes make the associated feature screening even challenging. In this article, we utilize the overlapping group screening (OGS) approach to select important G-E interactions related to clinical survival outcomes by incorporating the gene pathway information under a joint modeling framework. RESULTS: Simulation studies under various scenarios are carried out to compare the performances of our proposed method with some commonly used methods. In the real data applications, we use our proposed method to identify G-E interactions related to the clinical survival outcomes of patients with head and neck squamous cell carcinoma, and esophageal carcinoma in The Cancer Genome Atlas clinical survival genetic data, and further establish corresponding survival prediction models. Both simulation and real data studies show that our method performs well and outperforms existing methods in the G-E interaction selection, effect estimation, and survival prediction accuracy. CONCLUSIONS: The OGS approach is useful for selecting important environmental factors, genes and G-E interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The same idea of the OGS approach can apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes.

MOTIVATION: In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant feature screening difficult. Several independence screening methods have been developed, but they fail to account for gene-gene dependency information, and may be sensitive to outlying feature data. RESULTS: We improve the inverse probability-of-censoring weighted (IPCW) Kendall's tau statistic by using Google's PageRank Markov matrix to incorporate feature dependency network information. Also, to tackle outlying feature data, the nonparanormal approach transforming the feature data to multivariate normal variates are utilized in the graphical lasso procedure to estimate the network structure in feature data. Simulation studies under various scenarios show that the proposed network-adjusted weighted Kendall's tau approach leads to more accurate feature selection and survival prediction than the methods without accounting for feature dependency network information and outlying feature data. The applications on the clinical survival outcome data of diffuse large B-cell lymphoma and of The Cancer Genome Atlas lung adenocarcinoma patients demonstrate clearly the advantages of the new proposal over the alternative methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging. RESULTS: In this article, we propose an inverse probability-of-censoring weighted Kendall's tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall's partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall's partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers. AVAILABILITY AND IMPLEMENTATION: R-package 'IPCWK' is available to implement this method, together with a reference manual describing how to perform the 'IPCWK' package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computation of function values is critical for designing datapath units in digital signal processors (DSP) and graphics processing units (GPU). Most function computation methods requires lookup tables (LUT) and simple arithmetic components. In table-bound methods, LUT size takes a significant portion of total hardware area, in particular for high-precision applications. This paper presents a new multi-level lossless table decomposition to further reduce total table size in a recently proposed hierarchical multipartite (HMP) table method which is a generation of the prior bipartite/multipartite table-addition methods. Furthermore, hardware design parameters are optimized by jointly considering all the error sources. Experimental results shows that the proposed design has the chance of further reducing the total table size of HMP, which already has significant table-size saving over previous similar designs.

BACKGROUND: The development of a disease is a complex process that may result from joint effects of multiple genes. In this article, we propose the overlapping group screening (OGS) approach to determining active genes and gene-gene interactions incorporating prior pathway information. The OGS method is developed to overcome the challenges in genome-wide data analysis that the number of the genes and gene-gene interactions is far greater than the sample size, and the pathways generally overlap with one another. The OGS method is further proposed for patients' survival prediction based on gene expression data. RESULTS: Simulation studies demonstrate that the performance of the OGS approach in identifying the true main and interaction effects is good and the survival prediction accuracy of OGS with the Lasso penalty is better than the ordinary Lasso method. In real data analysis, we identify several significant genes and/or epistasis interactions that are associated with clinical survival outcomes of diffuse large B-cell lymphoma (DLBCL) and non-small-cell lung cancer (NSCLC) by utilizing prior pathway information from the KEGG pathway and the GO biological process databases, respectively. CONCLUSIONS: The OGS approach is useful for selecting important genes and epistasis interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The OGS approach is generally applicable to various types of outcome data (quantitative, qualitative, censored event time data) and regression models (e.g. linear, logistic, and Cox's regression models).

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA.

Function evaluation is an important arithmetic computation in many signal processing applications, such as special function units in modern graphics processing units (GPUs). Hardware implementations of function evaluation usually consists of lookup tables (LUT) and some simple arithmetic units of multipliers and/or adders. LUT usually takes a significant portion of total area cost, especially when function evaluators are allowed to compute several different arithmetic functions with shared arithmetic units where evaluation of each function needs separate LUT. In this paper, we focus on the category of table-addition (TA) function evaluators that are composed of two types of LUT, table of initial values (TI) and table of offset values (TO), followed by a multi-operand adder. It has been shown that multipartite table method (MP) has significant improvement over prior similar designs such as symmetric bipartite table methods (SBTM) and symmetric table addition methods (STAM) for applications with low-to-medium precision requirements. This paper presents an extension of MP, called hierarchical multipartite (HMP), which further reduces total table size by applying several levels of table decompositions. Furthermore, we perform the bit-width optimization by jointly considering the impacts of all error sources during the search of best table decompositions, leading to more efficient hardware design. Besides, a new lossless decomposition of TI is presented, resulting in additional saving of table size without incurring any extra errors. Experimental results show that the proposed design can efficiently reduce the total area cost in ASIC and FPGA implementations.

In the process of scalable video coding (SVC) decoding, large external memory bandwidth is required for SVC inter-layer prediction. In this paper, a low bandwidth decoder framework is proposed for SVC. Two main decoding schemes are developed to reduce the external memory bandwidth. Macroblock-level on-the-fly padding and on-line upsampling is proposed for SVC spatial scalability decoding. This scheme reduces 36% of decoding bandwidth and 34% of processing cycles. For SVC quality scalability, a layer-interleaving decoding scheme is proposed to eliminate all inter-layer prediction bandwidth which is 41-51% of decoding bandwidth. The corresponding hardware architectures of these two decoding schemes are also provided. This low bandwidth framework can save 33-52% of DRAM access power for SVC decoding.

For H.264/AVC decoder system, the motion compensation bandwidth comes from two parts, the reference data loading bandwidth and the equivalent bandwidth from DRAM access overhead latency. In this paper, a bandwidth-efficient cache-based MC architecture is proposed. It exploits both intra-MB and inter-MB data reuse and reduce up to 46% MC bandwidth compared to conventional scheme. To reduce the equivalent bandwidth from DRAM access overhead latency, the DRAM-friendly data mapping and access control scheme are proposed. They can reduce averagely 89.8% of equivalent DRAM access overhead bandwidth. The average MC burst length can be improved to 9.59 words/burst. The total bandwidth reduction can be up to 32∼71% compared to previous works.

High Definition (HD) H.264/AVC video compression is the emerging necessity on nowadays home entertainment environment and so on. However, Although B-frame coding scheme provides better quality, only P-frame encoders are presented due to too high complexity and memory requirement. In this paper, a frame-parallel encoding scheme based on B-frame’s data independency is proposed. It can largely reduce the system memory bandwidth and improve the processing capability. Then, the proposed IME and FME scheduling can further enhance the hardware utilization for frame-parallel scheme. Finally, a case study is given to show that the proposed scheme can largely reduce 66% system bandwidth compared to direct implementation from previous P-frame encoder.

Since motion-compensated temporal filtering (MCTF) becomes an important temporal prediction scheme in video coding algorithms, this paper presents an efficient temporal prediction engine which not only is the first MCTF hardware work but also supports traditional motion-compensated prediction (MCP) scheme to provide computation scalability. For the prediction stage of MCTF and MCP schemes, modified extended double current Frames is adopted to reduce the system memory bandwidth, and a frame-interleaved macroblock pipelining scheme is proposed to eliminate the induced data buffer overhead. In addition, the proposed update stage architecture with pipelined scheduling and motion estimation (ME)-like motion compensation (MC) with level C+ scheme can also save about half external memory bandwidth and eliminate irregular memory access for MC. Moreover, 76.4% hardware area of the update stage is saved by reusing the hardware resources of the prediction stage. This MCTF chip can process CIF 30 fps in real-time, and the searching range is [-32, 32) for 5/3 MCTF with four-decomposition level and also support 1/3 MCTF, hierarchical B-frames, and MCP coding schemes in JSVM and H.264/AVC. The gate count is 352-K gates with 16.8 KBytes internal memory, and the maximum operating frequency is 60 MHz.

Adaptive group-of-picture (GOP) structure is an important encoding tool in multi-level motion-compensated temporal filtering coding scheme. Compared to conventional fixed-GOP scheme, it can dynamically adapt the GOP size to enhance the coding performance based on each sequence's characteristics. But the existing adaptive GOP structure (AGS) algorithm proposed in JSVM requires huge computation complexity. In this paper, a fast AGS prediction algorithm is proposed. At first, based on the relationship among coding performance, GOP size and corresponding intra block ratio, a sub-GOP size prediction model for different decomposition levels is developed based on the encoded intra block ratio. Then, a prediction scheme is proposed to implement AGS by the sub-GOP size prediction model. It can predict the following sub-GOP size by current sub-GOP's information instead of searching all possible sub-GOP composition. The experimental results show that the proposed algorithm with linear threshold has almost equivalent coding performance as AGS in JSVM but only one-fourth computation complexity for 4-level interframe coding scheme is required.

Motion-compensated temporal filtering (MCTF) is an innovative prediction scheme for video coding and the core technology of scalable extension of H.264/AVC. The first MCTF and ME hardware work is in this paper. The proposed hardware not only can support the various coding schemes in JSVM and H.264 but also can adapt itself to provide rate-distortion-computation scalability. With the frame-level searching range data reuse and frame-interleaved MB pipelining scheme, external memory bandwidth are reduced 33%, and 10 Kbits buffer are saved. The proposed MCTF/ME hardware is of 350K gate counts and 30 KB internal buffer, which can perform four-level MCTF or H.264 P-/B-frames at CIF format

Motion-compensated temporal filtering (MCTF) is an open-loop prediction scheme, so the frame-level data reuse for MCTF is possible. In this paper, we propose two general frame-level data reuse schemes which can minimize the memory bandwidth of current and reference frames, respectively. And their relationships between the required memory bandwidth and the number of searching range buffers are also formulated under the constraint of the data dependency in joint scalable video model. Finally, we extend our analysis to pyramid MCTF and the impact of the inter-layer prediction scheme is also considered.

Motion-compensated temporal filtering (MCTF) is the core technology of the next generation scalable video coding schemes. There are two key steps in MCTF process, prediction step and update step. In this paper, an efficient scheme along with the hardware architecture of update step for VLSI implementation is proposed. The proposed update step scheme can reduce 55% off-chip memory access and turn the irregular access into regular access. 25% on-chip memory access is also reduced in deriving inverse motion vectors. 80% hardware area is saved by reusing the hardware resources of prediction step.

The memory bandwidth reduction for motion estimation is important because of the power consumption and limited memory bandwidth in video coding systems. In this paper, we propose a Level C+ scheme which can fully reuse the overlapped searching region in the horizontal direction and partially reuse the overlapped searching region in the vertical direction to save more memory bandwidth compared to the Level C scheme. However, direct implementation of the Level C+ scheme may conflict with some important coding tools and then induces a lower hardware efficiency of video coding systems. Therefore, we propose n-stitched zigzag scan for the Level C+ scheme and discuss two types of 2-stitched zigzag scan for MPEG-4 and H.264 as examples. They can reduce memory bandwidth and solve the conflictions. When the specification is HDTV 720p, where the searching range is [-128,128), the required memory bandwidth is only 54%, and the increase of on-chip memory size is only 12% compared to those of traditional Level C data reuse scheme.

To the best of authors' knowledge, this paper presents the first work on memory analysis of VLSI architectures for motion-compensated temporal filtering (MCTF). The open-loop MCTF prediction scheme has led the revolution for hybrid video coding methods that are mainly based on the close-loop MC prediction (MCP) scheme, and it also becomes the core technology of the coming video coding standard, MPEG-21 part 13-scalable video coding (SVC). In this paper, the macroblock (MB)-level and frame-level data reuse schemes are analyzed for the MCTF. The MB-level data reuse is especially for the motion estimation (ME), and the level C+ scheme is proposed, which can further reduce the memory bandwidth of the conventional level C scheme. Frame-level data reuse schemes for MCTF are proposed according to the open-loop prediction nature.

The motion-compensated temporal filtering (MCTF) is an innovative prediction scheme for video coding and has become the core technology of the coming video coding standard, MPEG-21 part 13 - scalable video coding (SVC). This paper provides the system analysis of MCTF for VLSI implementation, which includes computational complexity, external memory access, external storage size, and coding delay. The one-level MCTF is analyzed first, and a modified double current frames scheme is introduced to address the external memory access penalty that results from fractional-pel motion compensation (MC). Then the analysis is extended to multi-level MCTF, in which many important system issues will be explored. Finally, a real-life test case was given to compare the system requirements of many different MCTF schemes and the prediction scheme of H.264/AVC.

Sprite coding is a new objected-based coding technology proposed by MPEG-4 video standard. In this paper, we propose an unsupervised sprite coding system for sport videos, for example, tennis sequences. Our system can provide several important functions. First, the sprite of the background can be generated without any pre-processing masks in our system. Second, it can automatically segment the foreground and background in a video sequence. Third, it can provide the masks of the foreground objects and the tennis ball. The experimental results show that our system has a very good performance and the coding gain of our system compared with MPEG-4 advanced simple profile is 2.5 dB at the low bit rate (270 Kbps) and is 2 dB at the ultra low bit rate (70 Kbps). It can be used in the sport video coding at low bit rate and provides a object-based sport video sequence.