Abstract. A theorem of Kleitman states that a collection of binary vectors with diameter [Formula: see text] has cardinality at most that of a Hamming ball of radius [Formula: see text]. In this paper, we give a [Formula: see text]-analogue of it.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of definitive diagnostic tools. This paper introduces an innovative approach to predicting and analyzing Alzheimer's disease by constructing signed brain network models and leveraging signed graph neural network technologies. By modeling the brain network as a signed graph that incorporates both positive and negative correlations, we capture the nuanced interactions between brain regions more effectively than traditional methods. We utilize graph convolutional networks (GCNs) and their variants to process these signed brain networks, significantly improving the accuracy of Alzheimer's disease prediction. Comparative analysis reveals that the signed graph model outperforms its unsigned counterparts in diagnostic precision (with an improvement of at least 19%), emphasizing the importance of incorporating negative correlations in neural interactions. Furthermore, precisely because of the additional negative edge information that we can utilize both positive and negative attention matrices, derived from these prediction tasks, to determine important brain region biomarkers. This work is an attempt to systematically validate the role of negative information through comparisons of different signed graph variants, which holds particular promise for enhancing Alzheimer's disease diagnostic accuracy at early stages. We believe that this approach will have significant clinical applications in the future.

Elementary trapping sets (ETSs) are the main culprits of the performance of low-density parity-check (LDPC) codes in the error floor region. Due to their large quantities and complex structures, ETSs are difficult to analyze. This paper studies the impact of the distance between cycles on ETSs, focusing on two special graph classes: theta graphs and dumbbell graphs, which correspond to cycles with negative and non-negative distances, respectively. We determine the Turán numbers of these graphs and prove that increasing the distance between cycles can eliminate more ETSs. Additionally, using the linear state-space model and spectral theory, we prove that increasing the length of cycles or distance between cycles decreases the spectral radius of the system matrix, thereby reducing the harmfulness of ETSs. This is consistent with the conclusion obtained using Turán numbers. For specific cases when removing two 6-cycles with distance of -1, 0 and 1, respectively, we calculate the sizes, spectral radii, and error probabilities of ETSs. These results confirm that the performance of LDPC codes improves as the distance between cycles increases. Furthermore, we design the PEG-CYCLE algorithm, which greedily maximizes the distance between cycles in the Tanner graph. Numerical results show that the QC-LDPC codes constructed by our method achieve performance comparable to or even superior to state-of-the-art construction methods.

The weight spectrum plays a crucial role in the performance of error-correcting codes. Pre-transformation with an upper-triangular matrix improves the weight spectrum of polar codes while retaining polarization. However, a theoretical analysis to quantify the improvement for pre-transformed rate-compatible polar codes is missing. In this paper, we calculate the average spectrum of random upper-triangular pre-transformed shortened and punctured polar codes. Our approach tran-scends the limitations imposed by partial ordering and specific rate matching patterns. A key feature of our approach is its polynomial complexity in relation to the code length, making it computationally feasible. Simulation results affirm that our findings provide an accurate approximation on the performance of pre-transformed rate-compatible polar codes.

The code spectrum of a linear code provides insight into its optimal performance. By leveraging the lower-triangular affine group (LTA) of decreasing monomial codes in conjunction with the one-variable descendant (ovd) relation, we introduce a novel subgroup of LTA that can identify additional cosets with identical weight distributions. By exploiting this algebraic structure, we demonstrate that the group action on a coset set is transitive. Our method advances previous research, reducing complexity by several times in many cases of code length$N=128$and$N=256$.

In this paper, we establish the partial orders (POs) of rate-compatible polar codes under both the binary erasure channel (BEC) and the binary memoryless symmetric channel (BMSC). Firstly, we define the POs for rate-compatible polar codes under block rate matching. Additionally, we demonstrate that certain POs for mother code lengths remain valid under rate matching in the BEC. Finally, leveraging the existing POs in the BEC, we derive POs in the BMSC under block rate matching.

The weight spectrum plays a crucial role in the performance of error-correcting codes. Despite substantial theoretical exploration into polar codes with mother code length, a framework for the weight spectrum of rate-compatible polar codes remains elusive. In this paper, we address this gap by enumerating the number of minimum-weight codewords for quasi-uniform punctured, Wang-Liu shortened, and bit-reversal shortened decreasing polar codes. Notably, our algorithms operate with polynomial complexity relative to the code length. Simulation results affirm that our discoveries provide an accurate approximation on the performance of rate-compatible polar codes.

It is known that windowed decoding (WD) can effectively balance the performance and complexity of spatially coupled low-density parity-check (LDPC) codes. In this study, we show that information can propagate in a wave-like manner at a constant speed under WD. Additionally, we provide an upper bound for the information propagation speed on the binary erasure channel, which can assist in designing the number of iterations required within each window.

MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and cognitive decline. While graph convolutional networks (GCNs) have emerged as popular tools for AD diagnosis due to their ability to handle structural information and fuse multi-modal features, deep learning approaches face significant challenges including the requirement for large datasets and sensitivity to unbalanced label distributions in AD research. To address these limitations and enhance the flexibility of GCNs, we propose a graph convolutional network based on the meta-learning paradigm (ADMGCN) for early AD diagnosis. This approach incorporates weighting and dimensionality reduction to improve performance, storage, and training efficiency. By leveraging meta-learning, we sample subjects to create numerous label-balanced tasks, maximizing data utilization and mitigating the impact of label imbalance. Additionally, the meta-learning framework enables rapid adaptation to new tasks and facilitates independent testing of the GCN. RESULTS: Our model, ADMGCN, was extensively validated on the Alzheimer's Disease Neuroimaging Initiative datasets. It achieved a maximum accuracy of 73.7% in the multi-classification task for early AD diagnosis. In three binary classification tasks, the model also demonstrated strong performance, achieving accuracies of 92.8%, 88.0%, and 79.6%, respectively. These results confirm that the proposed method provides an effective approach and worthwhile support for the early diagnosis of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: ADMGCN is freely available at https://github.com/WendySun16/ADMGCN.

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably increased with the increase of drugs numbers, so the accurate prediction of toxic side effects of drug combinations is equally important. In this paper, we built a Metapath-based Aggregated Embedding Model on Single Drug-Side Effect Heterogeneous Information Network (MAEM-SSHIN), which extracts feature from a heterogeneous information network of single drug side effects, and a Graph Convolutional Network on Combinatorial drugs and Side effect Heterogeneous Information Network (GCN-CSHIN), which transforms the complex task of predicting multiple side effects between drug pairs into the more manageable prediction of relationships between combinatorial drugs and individual side effects. MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. This integration enhances prediction accuracy, efficiency, and scalability. Our experimental results demonstrate that this combined framework outperforms existing methodologies in predicting side effects, and marks a significant advancement in pharmaceutical research.

In this study, we analyze the characteristics of scheduling sequences for layered belief propagation (LBP) that can result in efficient decoding of low-density parity-check (LDPC) codes. Specifically, we claim that scheduling sequences leading to high decoding efficiency should prioritize updating check nodes with lower error probabilities aggregated from neighboring variable nodes. We prove this conclusion separately on both the BEC and the BI-AWGN channels. Some observable characteristics in “good” scheduling sequences regarding row weights, rows connected to punctured columns, and column weights can serve as corollaries to this conclusion. By comprehensively considering these characteristics of good scheduling, we design a multi-sequence nested scheduling scheme of layered decoding for 5G New Radio (NR) LDPC codes. The proposed schemes can obtain scheduling sequences for various rates of rate-compatible LDPC codes using small hardware storage. What’s more, by respectively storing double-sequence, triple-sequence, or more sequences to obtain scheduling sequences at various code rates, a trade-off can be made between decoder storage and decoding performance. Experimental results demonstrate that the proposed scheme achieves performance improvements compared to existing scheduling schemes at nearly all code rates.

The emergence of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, only 15-30% of the cancer patients respond to ICI treatment, which stimulates and enhances host immunity to eliminate tumor cells. ICI treatment is very expensive and has potential adverse reactions; therefore, it is crucial to develop a method which enables to accurately and rapidly assess a patient's suitability before ICI treatment. We complied germline whole-genome sequencing (WES) data of 37 melanoma patients who have been treated with ICIs and sequenced in our lab previously, and the WES data of other 700 ICI-treated cancer patients in public domain. Using these data, we proposed a novel double-channel attention neural network (DANN) model to predict cancer ICI-response and validate the predictions. DANN achieved a mean accuracy and AUC of 0.95 and 0.98, respectively, which outperformed traditional machine learning methods. Enrichment analysis of the DANN-identified genes indicated that cancer patients whose in-born genomic variants might mainly affect host immune system in a wide-ranging manner, and then affect ICI response. Finally, we found a set of 12 genes bearing genomic variants were significantly associated with cancer patient survivals after ICI treatment.

Drug combinations are vital for treating complex diseases and advancing drug development, but accurately identifying synergistic combinations remains a significant challenge. Although graph neural networks (GNNs) have recently been used to predict drug combinations, the complex interactions between drugs and multimodal data (e.g., target proteins) and the prevalent high-order relations among drugs have yet to be fully exploited. The hypergraph offers a natural methodology for modeling high-order relations and provides profound insights for multimodal fusion. Here, we introduce the multimodal relational hypergraph neural network (MRHGNN), a novel framework for predicting synergistic drug combinations. Specifically, we design a dual-channel architecture to capture the physicochemical attributes of drugs and their interactive synergies, thereby facilitating the generation of multimodal drug representations. To obtain comprehensive representations of drugs, we use an attention mechanism to explore complementarity among multimodal drug embeddings. In addition, the unified framework jointly learns primary and self-supervised learning tasks, fostering a robust predictive capability. Experimental results demonstrate that MRHGNN accurately predicts synergistic drug combinations, and the effectiveness of the dual-channel setup and motif structures has been validated through ablation studies. Further literature searches illustrate that our model holds significant promise in accelerating the discovery of novel synergistic drug combinations, particularly in cancer therapy. This study not only introduces a novel computational tool but also paves the way for advanced methodologies in drug discovery and development.

In this paper, we establish the second-order randomized identification capacity (RID capacity) of the Additive White Gaussian Noise Channel (AWGNC). On the one hand, we obtain a refined version of Hayashi's theorem to prove the achievability part. On the other, we investigate the relationship between identification and channel resolvability, then we propose a finer quantization method to prove the converse part. Consequently, the second-order RID capacity of the AWGNC has the same form as the second-order transmission capacity. The only difference is that the maximum number of messages in RID scales double exponentially in the block length.

In this paper, we define partial orders (POs) of polar codes based on the Bhattacharyya parameter and the bit-error probability, respectively. These POs are applicable to arbitrary binary memoryless symmetric channel (BMSC). Leveraging the extremal inequalities of polarization transformation, we derive new POs for BMSC based on the corresponding POs observed in the Binary Erasure Channel (BEC). We provide examples that demonstrate the inability of existing POs to deduce these novel POs. Furthermore, we establish upper bounds for the expansion parameter$\beta$if the polar codes constructed by$\beta- \mathbf{expansion}$method obey these POs.

Elementary trapping sets (ETSs) are the principal culprits for the performance of LDPC codes in the error floor region. Due to their large quantity, intricate structures, and high computational complexity, determining how to eliminate dominant ETSs in the design of LDPC codes has become a critical issue in improving error floor behavior. In this paper, we address this problem by avoiding particular theta graphs$(\theta(1,2,2)$and$\theta(2,2,2))$in the Tanner graph to eliminate specific ETSs. These can be characterized by a pivotal tool in graph theory - Turán numbers. Theoretically, we derive the exact Turán number for$\theta(1,2,2)$and demonstrate that all$(a, b)$-ETSs in a Tanner graph with variable-reaular degree$d_{L}(v)=\gamma$must satisfy the inequality$b\geq a\gamma-\frac{1}{2}a^{2}$. This result improves the lower bound previously obtained by Amirzade when the girth is 6. For girth 8, by constraining the relationship between any two 8-cycles in the Tanner graph, we establish a similar inequality$b\geq a\gamma-\frac{a(\sqrt{8a-7}-1)}{2}$. Our simulation results indicate that codes designed with these considerations exhibit improved performance and a lower error floor over additive white Gaussian noise channels.

Unequal error protection (UEP) codes can facilitate the transmission of messages with different protection levels. In this paper, we study the achievability bounds on UEP by the generalization of Gilbert-Varshamov (GV) bound. For the first time, we show that under certain conditions, UEP enhances the code rate comparing with time-sharing (TS) strategies asymptotically.

The number of low-weight codewords is critical to the performance of error-correcting codes. In 1970, Kasami and Tokura characterized the codewords of Reed-Muller (RM) codes whose weights are less than 2wmin, wherewminrepresents the minimum weight. In this paper, we extend their results to decreasing polar codes. We present the closed-form expressions for the number of codewords in decreasing polar codes with weights less than 2wmin. Moreover, the proposed enumeration algorithm runs in polynomial time with respect to the code length.

The automorphism ensemble (AE) decoding framework for polar codes attracts much attention recently. It decodes multiple permuted codewords with successive cancellation (SC) decoders in parallel and hence has lower latency compared to successive cancellation list (SCL) decoding. However, the AE decoding framework is ineffective for permutations falling into the lower-triangular affine (LTA) automorphism group, as they are invariant under SC decoding. Therefore, the block lower-triangular affine (BLTA) group was discovered to achieve better AE decoding performance. However, the equivalence of the BLTA group and the complete affine automorphism group was unresolved. Additionally, some automorphisms in BLTA group are also SC-invariant, thus are redundant in AE decoding. In this paper, we prove that BLTA group coincides with the complete automorphisms of decreasing polar codes that can be formulated as affine transformations. Also, we find a necessary and sufficient condition related to the block lower-triangular structure of transformation matrices to identify SC-invariant automorphisms. Furthermore, We present an algorithm that efficiently identifies all SC-invariant affine automorphisms under specific constructions.

Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes) improves the weight spectrum of Reed-Muller (RM) codes and polar codes significantly. However, a theoretical analysis to quantify the improvement is missing. In this paper, we provide asymptotic analysis on the number of low-weight codewords of the original and pre-transformed RM codes respectively, and prove that pre-transformation significantly reduces low-weight codewords, even in the order sense. For polar codes, we prove that the average number of minimum-weight codewords does not increase after pre-transformation. Both results confirm the advantages of pre-transformation.

The rapid development of Internet of Things (IoT) requires new massive random access protocols to support the massive devices. Recently, Polyanskiy [1] established an information-theoretic formulation of unsourced multiple access (uMAC) problem and proposed theoretical finite-length achievability and converse bounds. In this paper, we further study the tradeoff between the number of active users and the energy-per-bit of random Gaussian codebook under maximum likelihood decoding and use two methods to improve the finite-length achievability bounds when the number of users is large and small, respectively. Our new results improve the finite-length achievability bound by more than 0.15 dB when per-user probability of error (PUPE) is 10−1, and more than 0.25 dB when PUPE is 10−3.

Automorphism ensemble (AE) decoding for polar codes was proposed by decoding permuted codewords with successive cancellation (SC) decoders in parallel and hence has lower latency compared to that of successive cancellation list (SCL) decoding. However, some automorphisms are SC-invariant, thus are redundant in AE decoding. In this paper, we find a necessary and sufficient condition related to the block lower-triangular structure of transformation matrices to identify SC-invariant automorphisms. Furthermore, we provide an algorithm to determine the complete SC-invariant affine automorphisms under a specific polar code construction.

Identification capacities of randomized and deterministic identification were proved to exceed channel capacity for Gaussian channels with channel side information (CSI). In this work, we extend deterministic identification to the block fading channels without CSI by applying identification codes for both channel estimation and user identification. We prove that identification capacity is asymptotically higher than transmission capacity even in the absence of CSI. And we also analyze the finite-length performance theoretically and numerically. The simulation results verify the feasibility of the proposed blind deterministic identification in finite blocklength regime.

For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods.

Recently, a permutation-based successive cancellation (PSC) decoding framework for polar codes attracts much attention. It decodes several permuted codewords with indepen-dent successive cancellation (SC) decoders. Its latency thus can be reduced to that of SC decoding. However, the PSC framework is ineffective for permutations falling into the lower-triangular affine (LTA) automorphism group, as they are invariant under SC decoding. As such, a larger block lower-triangular affine (BLTA) group that contains SC-variant permutations was discovered for decreasing polar codes. But it was unknown whether BLTA equals the complete automorphism group. In this paper, we prove that BLTA equals the complete automorphisms of decreasing polar codes that can be formulated as affine transformations.

Polar codes are the first class of channel codes achieving the symmetric capacity of the binary-input discrete memoryless channels (B-DMC) with efficient encoding and decoding algorithms. But the weight spectrum of polar codes is relatively poor compared to Reed-Muller (RM) codes, which degrades their maximum-likehood (ML) performance. Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes), improves weight spectrum while retaining polarization. In this paper, the weight spectrum of upper-triangular pre-transformed polar codes is mathematically analyzed. In particular, we focus on calculating the number of low-weight codewords due to their impact on error-correction performance. Simulation results verify the accuracy of the analysis.

Research on the expander code shows that for a regular low-density parity-check (LDPC) code, the Tanner graph’s spectrum determines its properties, such as the minimum distance and the size of stopping sets. In this study, we demonstrate theoretically and experimentally that the performance of irregular LDPC codes is related to the graph spectrum. Our observations may provide an efficient metric to search for good irregular LDPC codes.

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer from two limitations for graph representation learning. First, they exploit non-smoothing node features which may result in suboptimal embedding and degenerated performance for graph classification. Second, they only exploit neighbor information but ignore global topological knowledge. Aiming to overcome these limitations simultaneously, in this paper, we propose a novel, flexible, and end-to-end framework, Graph Smoothing Splines Neural Networks (GSSNN), for graph classification. By exploiting the smoothing splines, which are widely used to learn smoothing fitting function in regression, we develop an effective feature smoothing and enhancement module Scaled Smoothing Splines (S3) to learn graph embedding. To integrate global topological information, we design a novel scoring module, which exploits closeness, degree, as well as self-attention values, to select important node features as knots for smoothing splines. These knots can be potentially used for interpreting classification results. In extensive experiments on biological and social datasets, we demonstrate that our model achieves state-of-the-arts and GSSNN is superior in learning more robust graph representations. Furthermore, we show that S3 module is easily plugged into existing GNNs to improve their performance.

BACKGROUND: In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. RESULTS: In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database. CONCLUSIONS: We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

Bioinformatics (2017) 33 (5): 733–739. DOI: https://doi.org/10.1093/bioinformatics/btw715 The publisher wishes to inform readers that the footnote for the † symbol was erroneously removed from the paper as first published. The paper has now been corrected online to include the footnote: ‘†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors’.

Motivation: Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now. Results: In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 ± 0.0054, 0.8301 ± 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement. Availability and Implementation: Matlab codes and dataset explored in this work are available at http://dwz.cn/4oX5mS . Contacts: [email protected] or [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

MOTIVATION: More and more evidences have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. RESULTS: In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease-lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease-lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research. AVAILABILITY: The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2.

Obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) construction is an essential part of routing. Recently, IC routing and related researches have been extended from Manhattan architecture (lambda2-geometry) to Y-/X-architecture (lambda3-lambda4-geometry) to improve the chip performance. This paper presents an O(n log n) heuristic, lambda-OAT, for obstacle-avoiding Steiner minimal tree construction in the lambda-geometry plane (lambda-OASMT). In this paper, based on obstacle-avoiding constrained Delaunay triangulation, a full connected tree is constructed and then embedded into lambda-OASMT by zonal combination. To the best of our knowledge, this is the first work addressing the lambda-OASMT problem. Compared with most recent works on OARSMT problem, lambda-OAT obtains up to 30-Kx speedup with quality solution. We have tested randomly generated cases with up to 10 K terminals and 10-K rectilinear obstacles within 4 seconds on a Sun V880 workstation (755-MHz CPU and 4-GB memory). The high efficiency and accuracy of lambda-OAT make it extremely practical and useful in the routing phase.

In recent years, the X-architecture is introduced to obtain better performance for integrated circuit physical design. This paper reformulates the global routing problem in X-architecture under the liquid routing model. Then, a dynamic resource assignment (Dra) method is presented to reduce potential vias. At last, a global router called DraXRouter, is designed, in which we adopt a dynamic-tabulist-based tree construction algorithm and a stochastic optimization strategy to gain high quality routing solution. Tested on ISPD'98 benchmarks, DraXRouter achieves better routing performance compared with two recent global routers.

Routing is one of the important phases in VLSI/ULSI physical design. The obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) construction is an essential part of routing since macro cells, IP blocks, and pre-routed nets are often regarded as obstacles in the routing phase. Efficient OARSMT algorithms can be employed in practical routers iteratively. Recently, IC routing and related researches have been extended from Manhattan architecture (λ2-geometry) to Y- / X-architecture (λ3- / λ4-geometry) to improve the chip performance. This paper presents an O(nlogn) heuristic, λ-OASMT, for obstacle-avoiding Steiner minimal tree construction in the λ-geometry plane. Based on obstacle-avoiding constrained Delaunay triangulation, a full connected tree is constructed and then embedded into λ-OASMT by a novel method called zonal combination. To the best of our knowledge, this is the first work addressing the λ-OASMT problem. Compared with two most recent works on OARSMT problem, λ-OASMT obtains up to 30Kx speedup with an even better quality solution. We have tested randomly generated cases with up to 1K terminals and 10K rectilinear obstacles within 3 seconds on a Sun V880 workstation (755MHz CPU and 4GB memory). The high efficiency and accuracy of λ-OASMT make it extremely practical and useful in the routing phase, as well as interconnect estimation in the process of floorplanning and placement.

CAD tools have become more and more important for integrated circuit (IC) design since a complicated system can be designed into a single chip, called system-on-a-chip (SOC), in which physical design tool is an essential and critical part. We try to consider the via minimization problem as early as possible in physical design. We propose a routing method focusing on minimizing vias while considering mutability and wire-length constraint. That is, in the global routing phase, we minimize the number of bends, which is closely related to the number of vias. Previous work only dealt with very small nets, but our algorithm is general for the nets with any size. Experimental results show that our algorithm can greatly reduce the count of bends for various sizes of nets while meeting the constraints of congestion and wire-length.

Routing is one of the important steps in VLSI/ULSI physical design. The rectilinear Steiner minimum tree (RSMT) construction is an essential part of routing. Since macro cells, IP blocks, and pre-routed nets are often regarded as obstacles in the routing phase, obstacle-avoiding RSMT (OARSMT) algorithms are useful for practical routing applications. This paper focuses on the OARSMT problem and presents an algorithm, named An-OARSMan, based on ant colony optimization. A greedy obstacle penalty distance (OP-distance) local heuristic is used in the algorithm and performed on the track graph. The algorithm has been implemented and tested on different kinds of obstacles. Experimental results show that An-OARSMan can handle complex obstacle cases including both convex and concave polygon obstacles with good length performance. It can always achieve the optimal solution in the cases with no more than 7 terminals.

Motivated by VLSI/ULSI routing applications, we present a heuristic for rectilinear Steiner minimal tree (RSMT) construction. We transform a rectilinear minimum spanning tree (RMST) into an RSMT by a novel method called polygonal contraction. Experimental results show that the heuristic matches or exceeds the solution quality of previously best known algorithms and runs much faster.