Aiding design and test optimization of analog circuits requires accurate models that can reliably capture complex dependencies of circuit performances on essential circuit and device parameters, and test signatures. We present a novel Bayesian learning technique, namely relevance vector and feature machine (RVFM), for characterizing analog circuits with sparse statistical regression models. RVFM not only produces accurate models learned from a moderate amount of simulation or measurement data, but also computes a probabilistically inferred weighting factor quantifying the criticality of each parameter as part of the overall learning framework, hence offering a powerful enabler for variability modeling, failure diagnosis, and test development. Compared to other popular learning-based techniques, the proposed RVFM produces more accurate models, requires less amount of training data, and extracts more reliable parametric ranking. The effectiveness of RVFM is demonstrated in terms of the statistical variability modeling of a low-dropout regulator (LDO) and the built-in self-test (BIST) development of a charge-pump phase-locked loop (PLL).

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines (SVMs), the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric SVM classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using four case studies: 1) dc/dc converter ripple noise analysis; 2) phase-locked loop lock-time verification; 3) reliability analysis of a ring oscillator with respect to process variations and initial conditions; and 4) prediction of chip peak temperature using a limited number of on-chip temperature sensors.

Formal methods such as reachability analysis often suffer from state space explosion in the verification of complex analog circuits. This paper proposes a parallel hierarchical SMT-based reachability analysis technique based on circuit decomposition. Circuits are systematically decomposed into subsystems with less complex transient behaviors which can be solved in parallel. Then a simulation-assisted SMT-based reachability analysis approach is adopted to conservatively approximate the reachable spaces in each subsystem with support function representations. We formally develop a general decomposition algorithm without overapproxmiation in system reconstruction. The efficiency of this general methodology is further optimized with an efficient parallel implementation strategy.

The emergence of digitally-intensive analog circuits introduces new challenges to formal verification due to increased digital design content, and non-ideal digital effects such as finite resolution, round-off error and overflow. We propose a machine learning approach to convert digital blocks to conservative analog approximations via the use of kernel ridge regression. These learned models are then adopted in a hybrid formal reachability analysis framework where the support function based manipulations are developed to efficiently handle the large linear portion of the design and the more general satisfiability modulo theories technique is applied to the remaining nonlinear portion. The efficiency of the proposed method is demonstrated for the locked time verification of a digitally intensive phase locked loop.

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines, the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric support vector machine classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using three case studies: PLL lock-time verification, SRAM yield analysis and prediction of chip peak temperature using a limited number of on-chip temperature sensors.

UNLABELLED: Bioinformatics involves the collection, organization and analysis of large amounts of biological data, using networks of computers and databases. Developing countries in the Asia-Pacific region are just moving into this new field of information-based biotechnology. However, the computational infrastructure and network bandwidths available in these countries are still at a basic level compared to that in developed countries. In this study, we assessed the utility of a BitTorrent-based Peer-to-Peer (btP2P) file distribution model for automatic synchronization and distribution of large amounts of biological data among developing countries. The initial country-level nodes in the Asia-Pacific region comprised Thailand, Korea and Singapore. The results showed a significant improvement in download performance using btP2P--three times faster overall download performance than conventional File Transfer Protocol (FTP). This study demonstrated the reliability of btP2P in the dissemination of continuously growing multi-gigabyte biological databases across the three Asia-Pacific countries. The download performance for btP2P can be further improved by including more nodes from other countries into the network. This suggests that the btP2P technology is appropriate for automatic synchronization and distribution of biological databases and software over low-bandwidth networks among developing countries in the Asia-Pacific region. AVAILABILITY: http://everest.bic.nus.edu.sg/p2p/

BACKGROUND: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW. RESULTS: Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds. CONCLUSION: The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.

BACKGROUND: Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families. RESULTS: The performance of these descriptor-sets were ranked by Matthews correlation coefficient (MCC), and categorized into two groups based on their performance. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets. CONCLUSION: Our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors.