Critical assessment of protein intrinsic disorder prediction.

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Critical assessment of protein intrinsic disorder prediction.

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Performance of TCP Congestion Predictors as Loss Predictors

In the context of TCP, several researchers have proposed heuristics to detect or predict congestion in the network. In this paper, the term congestion predictors refers to such heuristics. Past proposals require TCP sender to reduce its window size when congestion is detected or predicted (otherwise, the heuristic may dictate that the sender window be held constant or increased). The proposed heuristics to detect/predict congestion typically use simple statistics on observed round-trip times and/or observed throughput. The primary objective of this paper is to investigate the ability of the congestion predictors to predict a packet loss. Our measurements indicate that the three congestion predictors studied in this paper are often poor in their ability to predict a packet loss due to congestion. To arrive at this conclusion we measure the frequency with which the predictors predict congestion, and how often they predict congestion just before a packet loss. A study of the variations in..

Performance of TCP Congestion Predictors as Loss Predictors

In the context of TCP, several researchers have proposed heuristics to detect or predict congestion in the network. In this paper, the term congestion predictors refers to such heuristics. Past proposals require TCP sender to reduce its window size when congestion is detected or predicted (otherwise, the heuristic may dictate that the sender window be held constant or increased). The proposed heuristics to detect/predict congestion typically use simple statistics on observed round-trip times and/or observed throughput. The primary objective of this paper is to investigate the ability of the congestion predictors to predict a packet loss. Our measurements indicate that the three congestion predictors studied in this paper are often poor in their ability to predict a packet loss due to congestion. To arrive at this conclusion we measure the frequency with which the predictors predict congestion, and how often they predict congestion just before a packet loss. A study of the variations in..

Combining Branch Predictors

One of the key factors determining computer performance is the degree to which the implementation can take advantage of instruction-level parallelism. Perhaps the most critical limit to this parallelism is the presence of conditional branches that determine which instructions need to be executed next. To increase parallelism, several authors have suggested ways of predicting the direction of conditional branches with hardware that uses the history of previous branches. The different proposed predictors take advantage of different observed patterns in branch behavior. This paper presents a method of combining the advantages of these different types of predictors. The new method uses a history mechanism to keep track of which predictor is most accurate for each branch so that the most accurate predictor can be used. In addition, this paper describes a method of increasing the usefulness of branch history by hashing it together with the branch address. Together, these new tec..