nils-herrmann/clean_scirepeval huggingface dataset:
Prediction of Contact Maps by Recurrent Neural Network Architectures and Hidden Context Propagation From All Four Cardinal Corners

Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.  We develop a new set of e xible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network parameterizations of a class of Bayesian networks we call generalized input-output HMMs. For the specic case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of and 45% of distant contacts at , for proteins of length up to 300.  The contact map predictor will be made available through http://promoter.ics.uci.edu/BRNNPRED/ as part of an existing suite of proteomics predictors.
DNA Microarrays and Gene Expression: DNA array formats

Array technologies monitor the combinatorial interaction of a set of molecules, such as DNA fragments and proteins, with a predetermined library of molecular probes. The currently most advanced of these technologies is the use of DNA arrays, also called DNA chips, for simultaneously measuring the level of the mRNA gene products of a living cell. This method, gene expression profiling, is the major topic of this book. In its most simple sense, a DNA array is defined as an orderly arrangement of tens to hundreds of thousands of unique DNA molecules (probes) of known sequence. There are two basic sources for the DNA probes on an array. Either each unique probe is individually synthesized on a rigid surface (usually glass), or pre-synthesized probes (oligonucleotides or PCR products) are attached to the array platform (usually glass or nylon membranes). The various types of DNA arrays currently available for gene expression profiling, as well as some developing technologies, are summarized here. In situ synthesized oligonucleotide arrays The first in situ probe synthesis method for manufacturing DNA arrays was the photolithographic method developed by Fodor et al .  and commercialized by Affymetrix Inc. (Santa Clara, CA). First, a set of oligonucleotide DNA probes (each 25 or so nucleotides in length) is defined based on its ability to hybridize to complementary sequences in target genomic loci or genes of interest. With this information, computer algorithms are used to design photolithographic masks for use in manufacturing the probe arrays.
Prediction of coordination number and relative solvent accessibility in proteins

Knowing the coordination number and relative solvent accessibility of all the residues in a protein is crucial for deriving constraints useful in modeling protein folding and protein structure and in scoring remote homology searches. We develop ensembles of bidirectional recurrent neural network architectures to improve the state of the art in both contact and accessibility prediction, leveraging a large corpus of curated data together with evolutionary information. The ensembles are used to discriminate between two different states of residue contacts or relative solvent accessibility, higher or lower than a threshold determined by the average value of the residue distribution or the accessibility cutoff. For coordination numbers, the ensemble achieves performances ranging within 70.6–73.9% depending on the radius adopted to discriminate contacts (6Å–12Å). These performances represent gains of 16–20% over the baseline statistical predictor, always assigning an amino acid to the largest class, and are 4–7% better than any previous method. A combination of different radius predictors further improves performance. For accessibility thresholds in the relevant 15–30% range, the ensemble consistently achieves a performance above 77%, which is 10–16% above the baseline prediction and better than other existing predictors, by up to several percentage points. For both problems, we quantify the improvement due to evolutionary information in the form of PSI‐BLAST‐generated profiles over BLAST profiles. The prediction programs are implemented in the form of two web servers, CONpro and ACCpro, available at http://promoter.ics.uci.edu/BRNN‐PRED/. Proteins 2002;47:142–153.
Global Gene Expression Profiling in Escherichia coliK12

Leucine-responsive regulatory protein (Lrp) is a global regulatory protein that affects the expression of multiple genes and operons in bacteria. Although the physiological purpose of Lrp-mediated gene regulation remains unclear, it has been suggested that it functions to coordinate cellular metabolism with the nutritional state of the environment. The results of gene expression profiles between otherwise isogenic lrp + andlrp − strains of Escherichia colisupport this suggestion. The newly discovered Lrp-regulated genes reported here are involved either in small molecule or macromolecule synthesis or degradation, or in small molecule transport and environmental stress responses. Although many of these regulatory effects are direct, others are indirect consequences of Lrp-mediated changes in the expression levels of other global regulatory proteins. Because computational methods to analyze and interpret high dimensional DNA microarray data are still an early stage, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe a Bayesian statistical framework for a posterior estimate of the standard deviation of gene measurements based on a limited number of replications. We also describe an algorithm to compute a posterior estimate of differential expression for each gene based on the experiment-wide global false positive and false negative level for a DNA microarray data set. This allows the experimenter to compute posterior probabilities of differential expression for each individual differential gene expression measurement.
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data.



We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t -test, provide a systematic inference approach that compares favorably with simple t -test or fold methods, and partly compensate for the lack of replication.
Machine Learning Structural and Functional Proteomics

While new high-throughput experimental techniques are being developed for proteomics applications (e.g. mass spectrometry, protein chips), it is clear that given the fundamental importance of proteins to biology, biotechnology, and medicine, computer methods that can rapidly sift through massive amounts of data and help determine the structure and function of a large number of proteins in a given genome remain important. We provide a brief overview of the application of machine learning methods to proteomic problems. In particular, we outline a novel strategy for the complete prediction of protein 3D coordinates. The strategy relies on three main successive stages: prediction of structural features, prediction of topology, and prediction of actual coordinates. We provide a progress report under this strategy and describe the corresponding suite of web servers available through http://promoter.ics.uci.edu/BRNN-PRED/. Applications to functional proteomics are brie y discussed.
A model for self-organizing large scale wireless networks

To determine what is the best communication protocol for a large-scale distributed wireless network, we need to model network activity given the node distribution, the links, their cost, and the optimization criterion for information routing. In this paper, we propose a novel model and use it in a simulation framework in order to determine the dynamic evolution of the interconnection pattern. Under a small set of realistic assumptions, we show that activity in the network rapidly converges in time to a "small-world" topology. In this sparse topology, two nodes are connected through a small number of hops. Hence, the topology is characterized by local interconnectivity and low-power consumption and dispersion. Ultra Wide Band radio emerges as an ideal transmission technique given its low emitted power and high precision in localization.
DNA Structure, Protein-DNA Interactions, and DNA-Protein Expression - Session Introduction

The session explores computational approaches to understanding the fundamental cycle of DNA $ Protein $ DNA by which DNA and proteins co-exist, co-regulate, and co-create each other. There is mounting evidence that DNA structural properties beyond the double helix signiicantly aaect its interactions with proteins and play an important role in a number of biological phenomena ranging from gene regulation to triplet repeat expansion diseases. The selected papers address the underlying structural and functional basis by which these interactions can be understood, modeled, and predicted. The study of DNA structure, protein-DNA interactions, and DNA-protein expression addresses the structural and functional basis underlying many areas of current signiicance, such as gene expression arrays, gene networks, genetic regulation, motif discovery, molecular recognition, and ultimately the regulated control of all life processes. As examples, the protein-DNA interactions of regulatory proteins provide the physical basis for the expression patterns studied by gene expression arrays; triplet repeat expansion diseases such as Hunting-ton's disease are due to the abnormal expansion of DNA triplets which h a ve very extreme structural properties; and the viral packaging of nucleotides into the protein shell provides the physical basis for some of the most virulent o f human pathogens. The interplay b e t ween DNA and proteins is the most fundamental of biological interactions, and has pervasive implications in biology, medicine, pharmacology, and biotechnology. The DNA$Protein$DNA cycle, by which DNA and proteins co-exist, co-regulate, and co-create each other, is a delicate balance between string and structure. The heteropolymer nature of both DNA and proteins allows for their compact representation and manipulation as sequences of characters drawn from alphabets of four and twenty c haracters respectively. Sequence analysis has been one of the most fruitful areas of computational biology, and much of our knowledge in areas as diverse as phylogenetic ancestry and DNA binding has been obtained by clever string manipulations. On the other hand,
The Shattered Self - The End of Natural Evolution

Throughout history, we have selected and manipulated the genomes of plants, animals, and even ourselves. Until now, however, such control could be exerted only at the level of the entire organism. Scientific and technological advances now allow us to manipulate genomes directly at the level of single genes and their constituents, with a speed and precision that far exceed what natural evolution has been able to achieve over the past 3.5 billion years. These advances open new possibilities for medicine, biotechnology, and society as a whole. We already have in vitro fertilization and animal cloning; in the future human cloning and the exploitation of embryonic stem cells, among other capabilities, may be routine. At the same time, we are developing machines that will surpass the human brain in raw computing power and building an interconnected world of information-processing devices that makes science fiction pale in comparison. In this book Baldi explores what it is about these phenomena that makes us so uneasy -- the shattering of the human self as we know it. Through evolution our brains have been wired to provide us with an inner sense of self, a feeling that each of us is a unique individual delimited by precise boundaries. We have also been wired to reproduce ourselves in a certain way. Baldi argues that this self-centered view of the world is scientifically wrong. Its past success lies in its being an adequate model during our evolutionary bootstrapping: a world without molecular biotechnology, human cloning, and the Internet. Eventually we must come to terms with the fact that genomes, computations, and mind are fluid, continuous entities, in both space and time. The boundary between the self and the world has begun to blur and ultimately may evaporate entirely. Baldi offers not predictions but an open-eyed exploration of our current state of knowledge and the possibilities that lie ahead.
Improved prediction of the number of residue contacts in proteins by recurrent neural networks

Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.
Flexibility of the genetic code with respect to DNA structure

The primary function of DNA is to carry genetic information through the genetic code. DNA, however, contains a variety of other signals related, for instance, to reading frame, codon bias, pairwise codon bias, splice sites and transcription regulation, nucleosome positioning and DNA structure. Here we study the relationship between the genetic code and DNA structure and address two questions. First, to which degree does the degeneracy of the genetic code and the acceptable amino acid substitution patterns allow for the superimposition of DNA structural signals to protein coding sequences? Second, is the origin or evolution of the genetic code likely to have been constrained by DNA structure?



We develop an index for code flexibility with respect to DNA structure. Using five different di- or tri-nucleotide models of sequence-dependent DNA structure, we show that the standard genetic code provides a fair level of flexibility at the level of broad amino acid categories. Thus the code generally allows for the superimposition of any structural signal on any protein-coding sequence, through amino acid substitution. The flexibility observed at the level of single amino acids allows only for the superimposition of punctual and loosely positioned signals to conserved amino acid sequences. The degree of flexibility of the genetic code is low or average with respect to several classes of alternative codes. This result is consistent with the view that DNA structure is not likely to have played a significant role in the origin and evolution of the genetic code.
Bidirectional Dynamics for Protein Secondary Structure Prediction

In this chapter, we have proposed two novel architectures for dealing with sequence learning problems in which data is not obtained from physical measurements over time. The new architectures remove the causality assumption that characterize current connectionist approaches to learning sequential translations. Using bidirectional recurrent neural networks (BRNNs) on the protein secondary structure prediction task appears to be very promising. Our performance is very close to the best existing systems although our usage of profiles is not as sophisticated. One improvement of our prediction system could be obtained by using profiles from the TrEMBL database.
Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework

We describe statistical methods based on thet test that can be conveniently used on high density array data to test for statistically significant differences between treatments. These t tests employ either the observed variance among replicates within treatments or a Bayesian estimate of the variance among replicates within treatments based on a prior estimate obtained from a local estimate of the standard deviation. The Bayesian prior allows statistical inference to be made from microarray data even when experiments are only replicated at nominal levels. We apply these new statistical tests to a data set that examined differential gene expression patterns in IHF+ and IHF− Escherichia coli cells (Arfin, S. M., Long, A. D., Ito, E. T., Tolleri, L., Riehle, M. M., Paegle, E. S., and Hatfield, G. W. (2000) ). These analyses identify a more biologically reasonable set of candidate genes than those identified using statistical tests not incorporating a Bayesian prior. We also show that statistical tests based on analysis of variance and a Bayesian prior identify genes that are up- or down-regulated following an experimental manipulation more reliably than approaches based only on a t test or fold change. All the described tests are implemented in a simple-to-use web interface called Cyber-T that is located on the University of California at Irvine genomics web site.
Matching Protein -Sheet Partners by Neural Networks

Predicting the secondary structure ( -helices, sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike -helices that are built up from one contiguous region of the polypeptide chain, -sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as antiparallel -sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a -sheet structure. Variations on these architecture, including also pro les and ensembles, are trained and tested via ve-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.
Assessing the accuracy of prediction algorithms for classification: an overview

We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.
Sequence analysis by additive scales: DNA structure for sequences and repeats of all lengths

DNA structure plays an important role in a variety of biological processes. Different di- and tri-nucleotide scales have been proposed to capture various aspects of DNA structure including base stacking energy, propeller twist angle, protein deformability, bendability, and position preference. Yet, a general framework for the computational analysis and prediction of DNA structure is still lacking. Such a framework should in particular address the following issues: (1) construction of sequences with extremal properties; (2) quantitative evaluation of sequences with respect to a given genomic background; (3) automatic extraction of extremal sequences and profiles from genomic databases; (4) distribution and asymptotic behavior as the length N of the sequences increases; and (5) complete analysis of correlations between scales.



We develop a general framework for sequence analysis based on additive scales, structural or other, that addresses all these issues. We show how to construct extremal sequences and calibrate scores for automatic genomic and database extraction. We show that distributions rapidly converge to normality as Nincreases. Pairwise correlations between scales depend both on background distribution and sequence length and rapidly converge to an analytically predictable asymptotic value. For di- and tri-nucleotide scales, normal behavior and asymptotic correlation values are attained over a characteristic window length of about 10-15 bp. With a uniform background distribution, pairwise correlations between empirically-derived scales remain relatively small and roughly constant at all lengths, except for propeller twist and protein deformability which are positively correlated. There is a positive (resp. negative) correlation between dinucleotide base stacking (resp. propeller twist and protein deformability) and AT-content that increases in magnitude with length. The framework is applied to the analysis of various DNA tandem repeats. We derive exact expressions for counting the number of repeat unit classes at all lengths. Tandem repeats are likely to result from a variety of different mechanisms, a fraction of which is likely to depend on profiles characterized by extreme structural features.