nils-herrmann/clean_scirepeval huggingface dataset:
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity

Small molecules play a fundamental role in organic chemistry and biology. They can be used to probe biological systems and to discover new drugs and other useful compounds. As increasing numbers of large datasets of small molecules become available, it is necessary to develop computational methods that can deal with molecules of variable size and structure and predict their physical, chemical and biological properties.



Here we develop several new classes of kernels for small molecules using their 1D, 2D and 3D representations. In 1D, we consider string kernels based on SMILES strings. In 2D, we introduce several similarity kernels based on conventional or generalized fingerprints. Generalized fingerprints are derived by counting in different ways subpaths contained in the graph of bonds, using depth-first searches. In 3D, we consider similarity measures between histograms of pairwise distances between atom classes. These kernels can be computed efficiently and are applied to problems of classification and prediction of mutagenicity, toxicity and anti-cancer activity on three publicly available datasets. The results derived using cross-validation methods are state-of-the-art. Tradeoffs between various kernels are briefly discussed.


AVAILABILITY
Datasets available from http://www.igb.uci.edu/servers/servers.html
State-driven energy optimization in wireless sensor networks

Most sensor network applications require quality of service guarantees on a network-wide basis, suggesting the need for global network cost optimization. The dynamic and nonuniform local states of individual nodes in sensor networks complicate global cost optimization. Here, we present an approach for optimizing global cost in sensor networks through greedy local decisions at each node, and we explore the benefits of this approach in reducing the idle listening at individual nodes in order to reduce the global network energy cost. We consider two representations for the local sensor node state: (1) number of descendants in the routing tree: and (2) number of descendants and duty cycle. For both state representations, we show through experiments on a testbed of 14 mica2 sensor nodes running ALPL that enabling nodes to set their listening mode according to their local state reduces global energy cost by 35% and provides more balanced energy consumption over the case of BMAC.
Sigmoid : Towards an Intelligent , Scalable , Software Infrastructure for Pathway Bioinformatics and Systems Biology

We describe a generative, scalable software infrastructure for pathway bioinformatics and systems biology. The Sigmoid modeling system is a three-tier architecture comprising distributed modules that implement pathway/cell model generation and simulation (Cellerator), a pathway modeling database (Sigmoid proper), a Web service-oriented middleware, a graphical user interface, and in the future, parameter optimization and other datamining technologies. Key to the design of the infrastructure is its scalability ensured by leveraging symbolic computer algebra and self-generation of database and other code from high-level representations such as UML schema. All Sigmoid modeling software components are available through: http://www.igb.uci.edu/servers/sb.html.
ChemDB: a public database of small molecules and related chemoinformatics resources

The development of chemoinformatics has been hampered by the lack of large, publicly available, comprehensive repositories of molecules, in particular of small molecules. Small molecules play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes in chemical genomics and systems biology, and for the screening and discovery of new drugs and other useful compounds.



We describe ChemDB, a public database of small molecules available on the Web. ChemDB is built using the digital catalogs of over a hundred vendors and other public sources and is annotated with information derived from these sources as well as from computational methods, such as predicted solubility and three-dimensional structure. It supports multiple molecular formats and is periodically updated, automatically whenever possible. The current version of the database contains approximately 4.1 million commercially available compounds and 8.2 million counting isomers. The database includes a user-friendly graphical interface, chemical reactions capabilities, as well as unique search capabilities.


AVAILABILITY
Database and datasets are available on http://cdb.ics.uci.edu.
The architecture of pre-mRNAs affects mechanisms of splice-site pairing.

The exon/intron architecture of genes determines whether components of the spliceosome recognize splice sites across the intron or across the exon. Using in vitro splicing assays, we demonstrate that splice-site recognition across introns ceases when intron size is between 200 and 250 nucleotides. Beyond this threshold, splice sites are recognized across the exon. Splice-site recognition across the intron is significantly more efficient than splice-site recognition across the exon, resulting in enhanced inclusion of exons with weak splice sites. Thus, intron size can profoundly influence the likelihood that an exon is constitutively or alternatively spliced. An EST-based alternative-splicing database was used to determine whether the exon/intron architecture influences the probability of alternative splicing in the Drosophila and human genomes. Drosophila exons flanked by long introns display an up to 90-fold-higher probability of being alternatively spliced compared with exons flanked by two short introns, demonstrating that the exon/intron architecture in Drosophila is a major determinant in governing the frequency of alternative splicing. Exon skipping is also more likely to occur when exons are flanked by long introns in the human genome. Interestingly, experimental and computational analyses show that the length of the upstream intron is more influential in inducing alternative splicing than is the length of the downstream intron. We conclude that the size and location of the flanking introns control the mechanism of splice-site recognition and influence the frequency and the type of alternative splicing that a pre-mRNA transcript undergoes.
Attention: Bits Versus Wows

The concept of surprise is central to sensory processing, adaptation and learning, attention, and decision making. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. Humans are strongly attracted to locations of high Bayesian surprise, with 72% of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84% when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
SCRATCH: a protein structure and structural feature prediction server

SCRATCH is a server for predicting protein tertiary structure and structural features. The SCRATCH software suite includes predictors for secondary structure, relative solvent accessibility, disordered regions, domains, disulfide bridges, single mutation stability, residue contacts versus average, individual residue contacts and tertiary structure. The user simply provides an amino acid sequence and selects the desired predictions, then submits to the server. Results are emailed to the user. The server is available at .
Global Gene Expression Profiling in Escherichia coli K12

The ArcAB two-component system of Escherichia coli regulates the aerobic/anaerobic expression of genes that encode respiratory proteins whose synthesis is coordinated during aerobic/anaerobic cell growth. A genomic study of E. coli was undertaken to identify other potential targets of oxygen and ArcA regulation. A group of 175 genes generated from this study and our previous study on oxygen regulation (Salmon, K., Hung, S. P., Mekjian, K., Baldi, P., Hatfield, G. W., and Gunsalus, R. P. (2003)  29837–29855), called our gold standard gene set, have p values <0.00013 and a posterior probability of differential expression value of 0.99. These 175 genes clustered into eight expression patterns and represent genes involved in a large number of cell processes, including small molecule biosynthesis, macromolecular synthesis, and aerobic/anaerobic respiration and fermentation. In addition, 119 of these 175 genes were also identified in our previous study of the fnr allele. A MEME/weight matrix method was used to identify a new putative ArcA-binding site for all genes of the E. coli genome. 16 new sites were identified upstream of genes in our gold standard set. The strict statistical analyses that we have performed on our data allow us to predict that 1139 genes in the E. coli genome are regulated either directly or indirectly by the ArcA protein with a 99% confidence level.
Three-stage prediction of protein ?-sheets by neural networks, alignments and graph algorithms

Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures.



We propose a modular approach to the problem of predicting/assembling protein beta-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods.


AVAILABILITY
http://www.igb.uci.edu/servers/psss.html.
MITOMAP: a human mitochondrial genome database—2004 update

MITOMAP (http://www.MITOMAP.org), a database for the human mitochondrial genome, has grown rapidly in data content over the past several years as interest in the role of mitochondrial DNA (mtDNA) variation in human origins, forensics, degenerative diseases, cancer and aging has increased dramatically. To accommodate this information explosion, MITOMAP has implemented a new relational database and an improved search engine, and all programs have been rewritten. System administrative changes have been made to improve security and efficiency, and to make MITOMAP compatible with a new automatic mtDNA sequence analyzer known as Mitomaster.
Statistical Distribution of Chemical Fingerprints

Binary fingerprints are binary vectors used to represent chemical molecules by recording the presence or absence of particular substructures, such as labeled paths in the 2D graph of bonds. Complete fingerprints are often reduced to a compressed format–of typical dimension n = 512 or n = 1024–by using a simple congruence operation. The statistical properties of complete or compressed fingerprints representations are important since fingerprints are used to rapidly search large databases and to develop statistical machine learning methods in chemoinformatics. Here we present an empirical and mathematical analysis of the distribution of complete and compressed fingerprints. In particular, we derive formulas that provide good approximation for the expected number of bits set to one in a compressed fingerprint, given its uncompressed version, and vice versa.
A principled approach to detecting surprising events in video

Primates demonstrate unparalleled ability at rapidly orienting towards important events in complex dynamic environments. During rapid guidance of attention and gaze towards potential objects of interest or threats, often there is no time for detailed visual analysis. Thus, heuristic computations are necessary to locate the most interesting events in quasi real-time. We present a new theory of sensory surprise, which provides a principled and computable shortcut to important information. We develop a model that computes instantaneous low-level surprise at every location in video streams. The algorithm significantly correlates with eye movements of two humans watching complex video clips, including television programs (17,936 frames, 2,152 saccadic gaze shifts). The system allows more sophisticated and time-consuming image analysis to be efficiently focused onto the most surprising subsets of the incoming data.
Hidden Markov models and neural networks

In this contribution, we discuss the application of machine learning methods, particularly hidden Markov models and artificial neural networks, to bioinformatics tasks. We begin with a brief summary of the two paradigms as computational and learning systems and describe the nature of the problems to which these two approaches can be applied. Then, we give some examples of their application to bioinformatics, briefly discuss the strengths and weaknesses of these two approaches, and provide some avenues for further reading. 
 
 
 
 
hidden Markov models; 
neural networks; 
machine learning
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data

Intrinsically disordered regions in proteins are relatively frequent and important for our understanding of molecular recognition and assembly, and protein structure and function. From an algorithmic standpoint, flagging large disordered regions is also important for ab initio protein structure prediction methods. Here we first extract a curated, non-redundant, data set of protein disordered regions from the Protein Data Bank and compute relevant statistics on the length and location of these regions. We then develop an ab initio predictor of disordered regions called DISpro which uses evolutionary information in the form of profiles, predicted secondary structure and relative solvent accessibility, and ensembles of 1D-recursive neural networks. DISpro is trained and cross validated using the curated data set. The experimental results show that DISpro achieves an accuracy of 92.8% with a false positive rate of 5%. DISpro is a member of the SCRATCH suite of protein data mining tools available through http://www.igb.uci.edu/servers/psss.html.
Chemoinformatics, drug design, and systems biology.

In spite of its central role between physics and biology, chemistry has remained in a backward stateof informatics development compared to its two close relatives. Computers, public databases, andlarge collaborative projects have become the pervasive hallmark of research in physics and biology.The Human Genome Project, for instance, required collaboration among dozens if not hundreds ofscientists across the world. And the resulting human DNA sequence, as well as a wealth of otherbiological information, are available for anyone to download from public repositories on the Web suchas GenBank, Swissprot, the PDB, and PubMed. Virtually every biologist today uses publicly availabletools, such as BLAST, to search sequence databases and analyze high-throughput data. Similarobservations can be made in physics with large collaborative eﬁorts in, for instance astronomy or high-energy physics. The Web itself was born at CERN, a European consortium with over half a century ofhistory, and the world largest particle physics laboratory. In stark contrast, large collaborative eﬁortsand public databases and software are comparatively absent from chemical research.
U-MAC: a proactive and adaptive UWB medium access control protocol

Ultra wide band (UWB) technology has received increasing recognition in recent years for its potential applications beyond radar technology to communication networks. UWB is a spread spectrum technology that requires careful coordination among communicating nodes to jointly control link power and transmission rates. Here, we present ultra wide band MAC (U-MAC), an adaptive medium access control (MAC) protocol for UWB in which nodes periodically declare their current state, so that neighbors can proactively assign power and rate values for new links locally in order to optimize global network performance. Simulations comparing U-MAC to the reactive approach confirm that U-MAC lowers link setup latency and control overhead, doubles the throughput and adapts better to high network loads. Simulations also reveal that the basic form of U-MAC favors nodes that are closer to the receiver. As a result, we also introduce novel mechanisms that control the radius around a receiver within which nodes can have fair access to it. We show through simulations the effect of the mechanisms on the tradeoff between network throughput and fair access.
Retroviruses and yeast retrotransposons use overlapping sets of host genes.

A collection of 4457 Saccharomyces cerevisiae mutants deleted for nonessential genes was screened for mutants with increased or decreased mobilization of the gypsylike retroelement Ty3. Of these, 64 exhibited increased and 66 decreased Ty3 transposition compared with the parental strain. Genes identified in this screen were grouped according to function by using GOnet software developed as part of this study. Gene clusters were related to chromatin and transcript elongation, translation and cytoplasmic RNA processing, vesicular trafficking, nuclear transport, and DNA maintenance. Sixty-six of the mutants were tested for Ty3 proteins and cDNA. Ty3 cDNA and transposition were increased in mutants affected in nuclear pore biogenesis and in a subset of mutants lacking proteins that interact physically or genetically with a replication clamp loader. Our results suggest that nuclear entry is linked mechanistically to Ty3 cDNA synthesis but that host replication factors antagonize Ty3 replication. Some of the factors we identified have been previously shown to affect Ty1 transposition and others to affect retroviral budding. Host factors, such as these, shared by distantly related Ty retroelements and retroviruses are novel candidates for antiviral targets.
Attention: Bits versus Wows

The concept of surprise is central to sensory processing, adaptation and learning, attention, and decision making. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. Humans are strongly attracted to locations of high Bayesian surprise, with 72% of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84% when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
Sigmoid: a software infrastructure for pathway bioinformatics and systems biology

Biological systems encompass everything from immune and nervous systems to ecosystems. Systems biology, however, focuses primarily on unraveling molecular systems at the level of pathways and groups of pathways in a cell and its neighboring cells. Here we address the challenge of creating an expert assistance system for modeling biological pathways, using current software technology to decrease development costs and complexity. Our goal is to provide computational support to biologists and computational scientists who need to create and explore predictive dynamical models of complex biological systems such as metabolic, gene-regulation, or signal-transduction pathways in living cells.
Exploring chemical space with computers: challenges and opportunities

Small molecules with at most a few dozen atoms play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes for perturbing and analyzing biological systems, and for the screening/design/discovery of new drugs. As datasets of small molecules become increasingly available, it becomes important to develop computational methods for the classification and analysis of small molecules and in particular for the prediction of their physical, chemical, and biological properties. We describe datasets and machine learning methods, in particular kernel methods, for chemical molecules represented by 1D strings, 2D graphs of bonds, and 3D structures. We demonstrate state-of-the-art results for the prediction of physical, chemical, or biological properties including the prediction of toxicity and anti-cancer activity. More broadly, we will discuss some of the challenges and opportunities for computer science, AI, and machine learning in chemistry.
Statistical detection of chromosomal homology using shared-gene density alone

Over evolutionary time, various processes including point mutations and insertions, deletions and inversions of variable sized segments progressively degrade the homology of duplicated chromosomal regions making identification of the homologous regions correspondingly difficult. Existing algorithms that attempt to detect homology are based on shared-gene density and colinearity and possibly also strand information.



Here, we develop a new algorithm for the statistical detection of chromosomal homology, CloseUp, which uses shared-gene density alone to fully exploit the observation that relaxing colinearity requirements in general is beneficial for homology detection and at the same time optimizes computation time. CloseUp has two components: the identification of candidate homologous regions followed by their statistical evaluation using Monte Carlo methods and data randomization. Using both artificial and real data, we compared CloseUp with two existing programs (ADHoRe and LineUp) for chromosomal homology detection and found that in general CloseUp compares favorably.


AVAILABILITY
CloseUp and supplementary information are available at http://www.igb.uci.edu/servers/cgss.html