SCARF: a biomedical association rule finding webserver.

The analysis of enormous datasets with missing data entries is a standard task in biological and medical data processing. Large-scale, multi-institution clinical studies are the typical examples of such datasets. These sets make possible the search for multi-parametric relations since from the plenty of the data one is likely to find a satisfying number of subjects with the required parameter ensembles. Specifically, finding combinatorial biomarkers for some given condition also needs a very large dataset to analyze. For fast and automatic multi-parametric relation discovery association-rule finding tools are used for more than two decades in the data-mining community. Here we present the SCARF webserver for generalized association rule mining. Association rules are of the form: a AND b AND … AND x → y, meaning that the presence of properties a AND b AND … AND x implies property y; our algorithm finds generalized association rules, since it also finds logical disjunctions (i.e., ORs) at the left-hand side, allowing the discovery of more complex rules in a more compressed form in the database. This feature also helps reducing the typically very large result-tables of such studies, since allowing ORs in the left-hand side of a single rule could include dozens of classical rules. The capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer's database of the Coalition Against Major Diseases (CAMD) in our recent publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300-307, 2017). Here we describe the webserver implementation of the algorithm.

The Graph of Our Mind.

Graph theory in the last two decades penetrated sociology, molecular biology, genetics, chemistry, computer engineering, and numerous other fields of science. One of the more recent areas of its applications is the study of the connections of the human brain. By the development of diffusion magnetic resonance imaging (diffusion MRI), it is possible today to map the connections between the 1-1.5 cm2 regions of the gray matter of the human brain. These connections can be viewed as a graph. We have computed 1015-vertex graphs with thousands of edges for hundreds of human brains from one of the highest quality data sources: the Human Connectome Project. Here we analyze the male and female braingraphs graph-theoretically and show statistically significant differences in numerous parameters between the sexes: the female braingraphs are better expanders, have more edges, larger bipartition widths, and larger vertex cover than the braingraphs of the male subjects. These parameters are closely related to the quality measures of highly parallel computer interconnection networks: the better expanding property, the large bipartition width, and the large vertex cover characterize high-quality interconnection networks. We apply the data of 426 subjects and demonstrate the statistically significant (corrected) differences in 116 graph parameters between the sexes.

High-resolution directed human connectomes and the Consensus Connectome Dynamics.

Here we show a method of directing the edges of the connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics", described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human connectome.

MetaHMM: A webserver for identifying novel genes with specified functions in metagenomic samples.

The fast and affordable sequencing of large clinical and environmental metagenomic datasets opens up new horizons in medical and biotechnological applications. It is believed that today we have described only about 1% of the microorganisms on the Earth, therefore, metagenomic analysis mostly deals with unknown species in the samples. Microbial communities in extreme environments may contain genes with high biotechnological potential, and clinical metagenomes, related to diseases, may uncover still unknown pathogens and pathological mechanisms in known diseases. While the species-level identification and description of the taxa in the samples do not seem to be possible today, we can search for novel genes with known functions in these samples, using numerous techniques, including artificial intelligence tools, like the hidden Markov models (HMMs). Here we describe a simple-to-use webserver, the MetaHMM, which is capable of homology-based automatic model-building for the genes to be searched for, and it also finds the closest matches in the metagenome. The webserver uses already highly successful building blocks: it performs multiple alignments by applying Clustal Omega, builds a hidden Markov model with HMMER components of hmmbuild and uses hmmsearch for finding similar sequences to the specified model in the metagenomes. The webserver is publicly available at https://metahmm.pitgroup.org.

Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method.

Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565-1567, 2015) presented a study of the associations between MRI-detected resting-state functional connectomes and behavioral data, based on the Human Connectome Project's (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural connectome-properties, based also on the Human Connectome Project's 500-subject dataset. For the connectome properties, we have focused on the structural (or anatomical) connectomes, instead of the functional connectomes. For the structural connectome analysis we have computed and publicly deposited structural braingraphs at the site http://braingraph.org . Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.

Comparing advanced graph-theoretical parameters of the connectomes of the lobes of the human brain.

Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or connectomes which were defined in the last twenty years for enormous networks-like the graph of the World Wide Web-with hundreds of millions of nodes. Since the connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women's connectomes than in men's. In the present contribution we compare numerous graph parameters in the three largest lobes-frontal, parietal, temporal-and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.

Human sexual dimorphism of the relative cerebral area volumes in the data of the human connectome project

The average human brain volume of males is larger than that of females. Several MRI voxel-based morphometry studies show that the gray matter/white matter ratio is larger in females. Here we have analyzed the recent public release of the Human Connectome Project, and by using the data of 511 subjects (209 men and 302 women), we have found that the volumes of numerous subcortical areas and most cortical areas, normalized by the total brain mask volume, are significantly larger in women than in men. Additionally, we have discovered that, for multiple brain regions, the correlation of the size of the left and right part is different between the sexes

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The dorsal striatum and the dynamics of the consensus connectomes in the frontal lobe of the human brain.

In the applications of the graph theory, it is unusual that one considers numerous, pairwise different graphs on the very same set of vertices. In the case of human braingraphs or connectomes, however, this is the standard situation: the nodes correspond to anatomically identified cerebral regions, and two vertices are connected by an edge if a diffusion MRI-based workflow identifies a fiber of axons, running between the two regions, corresponding to the two vertices. Therefore, if we examine the braingraphs of n subjects, then we have n graphs on the very same, anatomically identified vertex set. It is a natural idea to describe the k-frequently appearing edges in these graphs: the edges that are present between the same two vertices in at least k out of the n graphs. Based on the NIH-funded large Human Connectome Project's public data release, we have reported the construction of the Budapest Reference Connectome Server http://www.connectome.pitgroup.org that generates and visualizes these k-frequently appearing edges. We call the graphs of the k-frequently appearing edges "k-consensus connectomes" since an edge could be included only if it is present in at least k graphs out of n. Considering the whole human brain, we have reported a surprising property of these consensus connectomes earlier. In the present work we are focusing on the frontal lobe of the brain, and we report here a similarly surprising dynamical property of the consensus connectomes when k is gradually changed from k = n to k = 1: the connections between the nodes of the frontal lobe are seemingly emanating from those nodes that were connected to sub-cortical structures of the dorsal striatum: the caudate nucleus, and the putamen. We hypothesize that this dynamic behavior copies the axonal fiber development of the frontal lobe. An animation of the phenomenon is presented at https://youtu.be/wBciB2eW6_8.

SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification.

Summary: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here, we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification. By applying SECLAF for residue-sequences, we have reported [Methods (2018), https://doi.org/10.1016/j.ymeth.2017.06.034] the most accurate multi-label protein classifier to date (UniProt-into 698 classes-AUC 99.99%; Gene Ontology-into 983 classes-AUC 99.45%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution. Availability and implementation: The program SECLAF is implemented in Python, and is available for download, with example datasets at the website https://pitgroup.org/seclaf/. For Gene Ontology and UniProt based classifications a webserver is also available at the address above.

Comparative connectomics: Mapping the inter-individual variability of connections within the regions of the human brain.

The human braingraph, or connectome is a description of the connections of the brain: the nodes of the graph correspond to small areas of the gray matter, and two nodes are connected by an edge if a diffusion MRI-based workflow finds fibers between those brain areas. We have constructed 1015-vertex graphs from the diffusion MRI brain images of 392 human subjects and compared the individual graphs with respect to several different areas of the brain. The inter-individual variability of the graphs within different brain regions was discovered and described. We have found that the frontal and the limbic lobes are more conservative, while the edges in the temporal and occipital lobes are more diverse. Interestingly, a "hybrid" conservative and diverse distribution was found in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse. Similar studies concerning the human genome discovered more and less conservative sections of the DNA, opening up entirely new fields in genomics. We think that the present study is the first step in this direction in human connectomics. The clinical significance of the conservativity of a given cerebral area could be the higher sensitivity for traumas and developmental or neuro-degenerative events than the less conservative areas.

Near perfect protein multi-label classification with deep neural networks.

Biological sequences can be considered as data items of high-, non-fixed dimensions, corresponding to the length of those sequences. The comparison and the classification of biological sequences in their relations to large databases are important areas of research today. Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here we present two new ANNs with multi-label classification ability, showing impressive accuracy when classifying protein sequences into 698 UniProt families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).

The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain.

Consensus Connectome Dynamics (CCD) is a remarkable phenomenon of the human connectomes (braingraphs) that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server, which depicts the cerebral connections of n = 418 subjects with a frequency-parameter k: For any k = 1, 2, …, n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k = n through k = 1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure. We hypothesize that this growing structure copies the axonal development of the human brain. Here we show the robustness of the CCD phenomenon: it is almost independent of the particular choice of the set of underlying connectomes. This result shows that the CCD phenomenon is most likely a biological property of the human brain and not just a property of the data sets examined. We also present a simulation that well-describes the growth of the CCD structure: in our random graph model a doubly-preferential attachment distribution is found to mimic the CCD.

The braingraph.org database of high resolution structural connectomes and the brain graph tools.

Based on the data of the NIH-funded Human Connectome Project, we have computed structural connectomes of 426 human subjects in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights. The graphs are given in anatomically annotated GraphML format that facilitates better further processing and visualization. For 96 subjects, the anatomically classified sub-graphs can also be accessed, formed from the vertices corresponding to distinct lobes or even smaller regions of interests of the brain. For example, one can easily download and study the connectomes, restricted to the frontal lobes or just to the left precuneus of 96 subjects using the data. Partially directed connectomes of 423 subjects are also available for download. We also present a GitHub-deposited set of tools, called the Brain Graph Tools, for several processing tasks of the connectomes on the site http://braingraph.org.

Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer's database.

The concept of combinatorial biomarkers was conceived when it was noticed that simple biomarkers are often inadequate for recognizing and characterizing complex diseases. Here we present an algorithmic search method for complex biomarkers which may predict or indicate Alzheimer's disease (AD) and other kinds of dementia. We show that our method is universal since it can describe any Boolean function for biomarker discovery. We applied data mining techniques that are capable to uncover implication-like logical schemes with detailed quality scoring. The new SCARF program was applied for the Tucson, Arizona based Critical Path Institute's CAMD database, containing laboratory and cognitive test data for 5821 patients from the placebo arm of clinical trials of large pharmaceutical companies, and consequently, the data is much more reliable than numerous other databases for dementia. The results of our study on this larger than 5800-patient cohort suggest beneficial effects of high B12 vitamin level, negative effects of high sodium levels or high AST (aspartate aminotransferase) liver enzyme levels to cognition. As an example for a more complex and quite surprising rule: Low or normal blood glucose level with either low cholesterol or high serum sodium would also increase the probability of bad cognition with a 3.7 multiplier. The source code of the new SCARF program is publicly available at http://pitgroup.org/static/scarf.zip.

Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0.

Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3.0 of the server, generating the common edges of the connectomes of variously parameterizable subsets of the 1015-vertex connectomes of 477 subjects of the Human Connectome Project's 500-subject release. The consensus connectomes are downloadable in CSV and GraphML formats, and they are also visualized on the server's page. The consensus connectomes of the server can be considered as the "average, healthy" human connectome since all of their connections are present in at least k subjects, where the default value of [Formula: see text], but it can also be modified freely at the web server. The webserver is available at http://connectome.pitgroup.org.

Significant differences found in short nucleotide sequences of human intestinal metagenomes of Northern-European and Chinese Origin.

BACKGROUND: Metagenomic analysis of environmental and clinical samples is gaining considerable importance in today's literature. Changes in the composition of the intestinal microbial communities, relative to the healthy control, are reported in numerous conditions. METHODS: We have carefully analyzed the frequencies of the short nucleotide sequences in the metagenomes of two different enterotypes; namely of Chinese and European origins. RESULTS: We have identified 255 nucleotide sequences of length up to 9, such that their frequencies significantly differ in the two enterotypes examined. CONCLUSIONS: We have demonstrated that short nucleotide sequences are capable of differentiating enterotypes, and not only metagenomes, originating from healthy and diseased subjects. GENERAL SIGNIFICANCE: Our results may imply that the frequency-differences of certain short nucleotides have diagnostical value if properly applied for different clusters of metagenomes. "This article is part of a Special Issue entitled "Science for Life" Guest Editor: Dr. Austen Angell, Dr. Salvatore Magazù and Dr. Federica Migliardo".

How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain.

The human braingraph or the connectome is the object of an intensive research today. The advantage of the graph-approach to brain science is that the rich structures, algorithms and definitions of graph theory can be applied to the anatomical networks of the connections of the human brain. In these graphs, the vertices correspond to the small (1-1.5 cm2) areas of the gray matter, and two vertices are connected by an edge, if a diffusion-MRI based workflow finds fibers of axons, running between those small gray matter areas in the white matter of the brain. One main question of the field today is discovering the directions of the connections between the small gray matter areas. In a previous work we have reported the construction of the Budapest Reference Connectome Server http://connectome.pitgroup.org from the data recorded in the Human Connectome Project of the NIH. The server generates the consensus braingraph of 96 subjects in Version 2, and of 418 subjects in Version 3, according to selectable parameters. After the Budapest Reference Connectome Server had been published, we recognized a surprising and unforeseen property of the server. The server can generate the braingraph of connections that are present in at least k graphs out of the 418, for any value of k = 1, 2, …, 418. When the value of k is changed from k = 418 through 1 by moving a slider at the webserver from right to left, certainly more and more edges appear in the consensus graph. The astonishing observation is that the appearance of the new edges is not random: it is similar to a growing shrub. We refer to this phenomenon as the Consensus Connectome Dynamics. We hypothesize that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that are present only in a decreasing fraction of the subjects are gradually the newer connections in the individual brain development. An animation on the phenomenon is available at https://youtu.be/yxlyudPaVUE. Based on this observation and the related hypothesis, we can assign directions to some of the edges of the connectome as follows: Let Gk + 1 denote the consensus connectome where each edge is present in at least k+1 graphs, and let Gk denote the consensus connectome where each edge is present in at least k graphs. Suppose that vertex v is not connected to any other vertices in Gk+1, and becomes connected to a vertex u in Gk, where u was connected to other vertices already in Gk+1. Then we direct this (v, u) edge from v to u.

Nucleotide 9-mers characterize the type II diabetic gut metagenome.

Discoveries of new biomarkers for frequently occurring diseases are of special importance in today's medicine. While fully developed type II diabetes (T2D) can be detected easily, the early identification of high risk individuals is an area of interest in T2D, too. Metagenomic analysis of the human bacterial flora has shown subtle changes in diabetic patients, but no specific microbes are known to cause or promote the disease. Moderate changes were also detected in the microbial gene composition of the metagenomes of diabetic patients, but again, no specific gene was found that is present in disease-related and missing in healthy metagenome. However, these fine differences in microbial taxon- and gene composition are difficult to apply as quantitative biomarkers for diagnosing or predicting type II diabetes. In the present work we report some nucleotide 9-mers with significantly differing frequencies in diabetic and healthy intestinal flora. To our knowledge, it is the first time such short DNA fragments have been associated with T2D. The automated, quantitative analysis of the frequencies of short nucleotide sequences seems to be more feasible than accurate phylogenetic and functional analysis, and thus it might be a promising direction of diagnostic research.

Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's.

Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google's PageRank and the subsequent rise of the most popular search engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.