dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual graphs were then combined into one heterogeneous graph with possible multiple edges between two nodes. Additionally, each node has been characterized by a set of attributes such as ENSG ID, corresponding gene name, gene biotype, SNP ID, GWAS p-value, positive selection p-value and aggregated expression in 231 brain regions. Co-expression analysis. Co-expression in Alzheimer's disease and healthy individuals was computed based on the selected six Affymetrix GeneChip Human Genome U133 Plus 2. 0 microarray gene expression data sets  [28] [29] [30] [31] [32] [33]  from the Multi Experiment Matrix (MEM) database  34. Data sets were obtained in NetCDF format and stored as a "Gene expression microarray datasets in NETCDF format from MEM database" part of HENA data collection at figshare repository  20.
These data sets were chosen manually based on the sample annotations. Selected data sets contain samples from patients with neurodegenerative disorders including Alzheimer's disease (AD) and healthy individuals (HI) (E-MEXP-2280  28  (5 AD, 5 HI), E-GEOD-5281  29  (88 AD, 73 HI), E-GEOD-4757  30  (10 AD, 10 HI), E-GEOD-29652  31  (18 AD), E-GEOD-28146 32 (22 AD, 8 HI), E-GEOD-18309  33  (3 AD, 3 HI)). For further analysis we have selected only the samples related to the patients with Alzheimer's disease (AD) and healthy individuals (HI). We calculated the Spearman correlation between all pairs of probes in each data set and sort-ranked them based on the correlation value. In each data set the pair with the strongest correlation value received the highest rank (rank 1). Ranks obtained for each pair of probe sets in each data set were then normalized and aggregated using the Robust Rank Aggregation (RRA) method implemented in Robust Rank Aggreg R package  35.
A and ENSG. B, and a score representing the Spearman correlation coefficient. Co-expressions in each disease-related region is reported separately (see Online-only Table  1  for details). Epistasis analysis. Epistatic effects were computed in three cohorts. The disease-associated traits that were used included the change in ventricular volume, derived from successive brain MRIs, scores from a panel of cognitive traits tests and Braak staging reflecting the disease severity  57. The epistatic interactions were computed as the departure from addtitivity of the effects of pairs of SNPs on phenotype, as detailed below. Epistatic interactions between pairs of SNPs associated with change in ventricular volume  20  detected in ADNI consortium  10  patients were computed based on genotyping and longitudinal brain imaging data that were downloaded from the ADNI database  58  provided by the ADNI consortium.
The model was fitted by linear regression, and the presence of epistasis assessed based on the F test p-value of the coefficient for the multiplicative term, relative to the hypothesis of a null effect. The significance of each interacting SNP pair is characterized by p-value < 1e -8 for the association with changes related to ventricular volume and Braak score and p-value < 1e -5 for the association with cognitive traits. The computation was run in parallel at the HPC center of the Vital-IT group at SIB, using software versions optimized to the diverse processor (intel(R) Xeon(R)) architectures and parallelization paradigms (OpenMP, MPI, Hybrid) available in this compute cluster. Mapping of SNPs to genic and intergenic bins SNP IDs, for which genotype data were available, were mapped on genomic regions using the Ensembl database accessed via R package biomaRt ver.
Yeast two-hybrid data generation and analysis. The yeast two-hybrid PPI data set of proteins related to brain ageing was produced by the Aged Brain SYSBIO consortium  64. Studying the interactions between the proteins can reveal their function, and provide information about biological processes they participate in, including neurodegenerative diseases like Alzheimer's disease. All the baits were cloned using standard procedure after PCR amplification and the validity of the sequence was checked by full sequencing. The baits were used to screen a random-primed cDNA library constructed into the pP6. Human Brain (HBR) cDNA library. The prey fragments of the positive clones were amplified by PCR and sequenced at their 5′ and 3′ junctions. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI) using a fully automated procedure.
A confidence score (PBS, for Predicted Biological Score) was attributed to each interaction as previously described  65. The protein interactions from this publication have been submitted to the IMEx consortium  66  through IntAct 8 and assigned the identifier IM-26801. Protein identifiers from the resulting PPI data set were mapped onto ENSG IDs using R package gPro-fileR  36. The corresponding interactions were added to HENA data set in a form of interaction between two nodes ENSG. A-ENSG. B and a score representing PBS (please see Online-only Table  1  for details). Filtering of the protein-protein interactions from Intact data sets. Three data sets of human protein-protein interactions were downloaded from the IntAct database version 4. 2. 6 8 -a human interaction data set  67  , a data set of expert-curated PPIs based on proteins with an association to Alzheimer disease (http://www.
ebi. ac. uk/intact/query/annot:%22dataset:alzheimers%22), and a data set of PPI based on proteins with an established role in the presynapse (http://www. ebi. ac. uk/intact/query/annot:%22dataset:synapse%22). To keep interactions with medium or high confidence, as suggested by IntAct domain experts, we kept the data with the IntAct MI score ≥ 0. 45. Data sets for human interactions were selected based on the following search criteria "(taxid: 9606 (human)) AND intact-miscore: [0. 45 TO 1]". Since the data sets also contained some interactions between human proteins with proteins of other species, we additionally filtered curated data sets ADIA and SIA based on the human taxonomy ID 9606 to select human-specific interactions. Protein names from the original data sets were mapped to Ensembl ver.
93 gene name space (ENSG ID) using R package gProfileR  36. The interactions between protein. A and protein. B were added to the HENA data set in a form of interaction ENSG. A-ENSG. B with the corresponding MI score  68  (see Online-only Table  1  for details). Combining individual interaction data sets. Individual data sets of co-expression, epistasis and protein-protein interactions in the form of edge lists, obtained as described in the previous Methods sections, were combined into one data set. This data set can be represented as a heterogeneous graph, with nodes representing genes, and edges representing biological relations between the genes. HENA data set network structure  18, 19  is illustrated as a graph in Fig. 3  and represented as an example edge list in Table  4. Additionally, each node in the HENA data set is characterized by a set of attributes ("HENA NODE ATTRIBUTES" part of HENA data collection deposited at figshare repository)  20  , such as gene name, biotype  69  , GWAS p-value, positive selection p-value and gene expression in the 231 brain region.
Positive selection analysis. In order to characterize the evolutionary dynamics in genes that are associated with Alzheimer's disease, we analyzed the SNP data from IGAP  14, 70. We first mapped the 500 most significant SNPs onto their encoded human genes. As many SNPs mapped to the same genes, we removed duplicates, yielding a list of 42 unique genes. These genes were converted to ENSG IDs and 1 to 1 orthologs for each of the corresponding human genes were searched across 41 mammalian species described in the tree of mammalian species topology. When querying the Ensembl database, only genes with transcripts whose status is "known" and "coding" were retained. Only 36 of the genes matched these criteria. In cases for which more than one transcript was available per human gene, the longest one was retained.
Data record 8 -PPI from IntAct in human. The data set contains information about medium and highly confident protein-protein interactions in humans that are available from the IntAct molecular interaction database  8. The confidence level of each interaction is characterized by an IntAct MI score  68  ; larger scores correspond to a higher confidence level. A summary of this data record is shown in Online-only Table  1. This data set can be downloaded as an individual fail "intact_int. txt" located under section "Preprocessed PPI data sets from IntAct included in HENA" of HENA data collection deposited at figshare repository  20  and as a part of the integrated data collection  18, 19. Data record 9 -alzheimer's disease PPI from IntAct. Alzheimer's disease PPI from IntAct (ADIA) is a subset of the expert-curated PPI data set based on the proteins with an association to Alzheimer's disease  85  available from the IntAct molecular interaction database (http://www.
ebi. ac. uk/intact/query/annot:%22dataset:alzheimers%22, 2017)  8. The confidence level of each interaction is characterized by an IntAct MI score as described in Data record 8. Data were filtered as described in the Methods section. A summary of this data record is shown in Online-only Table  1. The filtered data set is available for download as a part of the integrated data collection  18, 19  and as an individual fail "alz_intact_int. txt" under "Preprocessed PPI data sets from IntAct included in HENA" section of HENA data collection deposited at Figshare repository  20. Data record 10 -synaptic PPI from IntAct. The synaptic PPI data set from IntAct (SIA) contains automatically selected protein-protein interactions from the IntAct database  8  with an established role in the presynapse (http://www.
ebi. ac. uk/intact/query/annot:%22dataset:synapse%22, 2017). A selected set of interactions is comprised of protein pairs where at least one protein has an established link to the synapse. The confidence level of each interaction is characterized by an IntAct MI score as described in Data record 8. The data set was filtered as described in the Methods section. A summary of this data set attributes is depicted in Online-only Table  1. The filtered data set is available for download as a part of the integrated data collection  18, 19  and as an individual fail "syn_intact_int. txt" under "Preprocessed PPI data sets from IntAct included in HENA" section of HENA data collection deposited at figshare repository  20. Data record 11 -aggregated information about the gene. The data record contains combined information about all nodes involved in the interactions described in the HENA data set  18, 19.
Each node in this data set represents a gene, protein, SNP or probe that is uniquely identified by its Ensembl gene name, and is characterized by a set of attributes (Fig. 3  and Table  2 ). This data record combines preprocessed GWAS data set related to Alzheimer's disease ("Preprocessed GWAS data included in HENA)  20  , a data set of genes showing positive selection ("Positive Darwinian selection")  20  , and a data set of gene expression in brain regions ("Aggregated gene expression in six whole-brain microarray datasets")  20. Individual gene attributes, i. e. ENSG ID, gene name, biotype  69  , SNP ID, GWAS p-value, positive selection p-value and expression in the 231 brain region are combined based on ENSG ID and represent a vector of 237 attributes for each node (see Table 2). A summary of this data is presented in Online-only Table  1.
The data record also contains information about the association of 11,632 SNPs mapped onto ENSGs with Alzheimer's disease  70  based on a genome-wide association study performed by the International Genomics of Alzheimer's Project  14. Aggregated data about each gene also includes the information about positive Darwinian selection produced by the AgedBrainSYSBIO consortium ("Positive Darwinian selection" data set)  20. Additionally, each gene in the data set is characterized by its biotype and gene name according to Ensembl database ver. 93.
HBTRC cohort samples were genotyped on the Illumina HumanHap650Y array (Illumina, Inc. , San Diego, CA, USA) by the Merck Research Laboratories. Genotype data (Version 01, 2011-01), available for 741 subjects and 555091 markers, were downloaded from the Sage Data Repository  61. The Sage Data Repository dataset entry  61  has been modified since the original dataset was downloaded at April 28 th 2015 from https://synapse. sagebase. org/#Synapse:syn4505. Thus we have stored the dataset used by us as part of the HENA data collection available via figshare repository  20. ADNI cohort samples were assayed using Human610-Quad BeadChip (Illumina, Inc. , San Diego, CA, USA), which features a genome-wide selection of 620901 tag SNPs. GenomeStudio v2009. 1 (Illumina) was used to generate SNP genotypes from bead intensity data  90.
adjustment of change in ventricular volume phenotype. Segmentation of cerebral ventricles and other brain structures on MRI images was performed at UCSF with the Freesurfer image analysis suite  94  , and the volumes reported in the ADNIMERGE data table  58. In our study we have used the content of this table downloaded from the ADNI repository on January 30, 2014. The evolution of ventricular volume over time in each subject was characterized by the slope of a linear regression line fitted on the individual's time-series of volume measurements, using the lm() function in R  95  , for a total of 621 subjects having at least two time-points, out of the 757 subjects with genotype data. To account for confounding effects of major covariates, a multiple linear model of the effects of age, gender and baseline disease status was fitted to the ventricle volume increase rates, using the lm() function in R  95.
Selecting reliable PPI. The current data set includes a yeast two-hybrid data set generated by the consortium and three data sets from IntAct database  8. The confidence of the PPI can be evaluated based on the corresponding score. Yeast two-hybrid PPIs are characterized by a PBS score  83, 84. The PBS relies on two different levels of analysis. Firstly, a local score takes into account the redundancy and independence of prey fragments, as well as the distribution of reading frames and stop codons in the overlapping fragments. Secondly, a global score takes into account the interactions found in all the screens performed using the same library. This global score represents the probability of an interaction being nonspecific. The score ranges from 0 to 10 (the smaller the score, the more specific the interaction).
In the HENA data set, we did not enforce any filtering on PBS, leaving the opportunity for the user to decide what threshold for the interactions is the most desirable. Interactions from the IntAct database are characterized by the MI score. It is based on the manual annotation of every instance of a binary interaction within the IntAct database, taking into account information about the interaction detection method, interaction type, and the number of publications the interaction has appeared in. The cumulative score is normalized between 0 and 1 across the entire IntAct database, with 1 representing an interaction with the highest confidence. The interactions added to HENA were selected based on the medium to high confidence level corresponding to MI score range [0. 45; 1]. Detecting positively selected genes.
Two types of tests were conducted to identify positively selected genes as described in the Methods section. The resulting p-values were adjusted for multiple comparisons using the FDR method. Genes evolving under a positive selection regime were chosen based on p-value ≤ 0. 05. Comparison with public databases. Although there is no alternative Alzheimer's disease-specific database or data set such as HENA available to our knowledge, we have selected for the comparison two widely used general-purpose databases that use network structure as underlying architecture of the data representation. We have compared the Alzheimer's disease-specific PPI, co-expression and epistatic interactions collected in HENA with the interactions of the matching type from STRING  99  and GeneMANIA 100. Comparisons were conducted for the pairs of interacting nodes.
While GeneMania reports interactions using ENSG IDs, STRING uses Ensembl protein identifiers. For the sake of consistent comparison we have mapped Ensembl protein identifiers to ENSG IDs. It is not a trivial task to find a suitable data source for the comparison of epistatic interactions due to the lack of such resource. We have compared the epistatic interactions with genetic interactions from GeneMania solely as the STRING database does not contain similar interaction types. We have excluded IGRIs from the comparison due to the lack of similar types of interactions in these two public databases. All data sets were prepared for the comparison by keeping only binary undirected interactions in the form of ENSG. A-ENSG. B. The overlapping interactions of the same type were counted and visualized using UpSetR R package  101  for the visualization of intersecting sets.
The results of the comparison are displayed in Fig. 5. Comparison of the computational data sets such as co-expression and epistasis is highly dependent on the conditions used to produce a particular data set. Therefore, substantial discrepancies are possible when comparing computational data sets obtained in different setups, i. e. disease-and phenotype-specific co-expression and epistatic interactions might differ from the interactions reported for other conditions. Additionally, the discrepancy between the number of overlapping interactions can be caused by the difference in how frequently data sources are being updated and criteria for the interactions to be included into a database. For example, in the case of the PPI comparison we found out examples of PPIs, such as MAD1L1 -LMO3 with a MI score of 0.
49, that is considered to be interaction with medium-high confidence by IntAct, but they were not present in STRING database as was expected because STRING includes interactions from IntAct. There are a few reasons why interactions may not appear in STRING. Firstly, STRING is updated every two years. Secondly, the data sets imported to STRING are re-benchmarked for each interaction A-B, taking into account the number of cliques and the number of interacting partners for both proteins A and B in the particular data set.
We also show how additional data sets can be used together with the HENA data set. The most straightforward way to approach this problem would be to use a supervised machine learning approach, where genes are labeled based on their association with Alzheimer's disease. Using the labeled set of genes we can train a model to find a decision boundary between two classes, and apply it to predict the association for the rest of the genes. Combining HENA with an additional data set. Defining a positive and negative class for the model: Despite the substantial number of studies that have been carried out in the field of Alzheimer's disease research, a set of confirmed positive and negative associations of genes and Alzheimer's disease is not yet well defined. The genes (and proteins as their products) can be defined as associated with the disease, for example, based on genome-wide association studies and based on domain-expert-curated knowledge.
We used information about the nodes from HENA to assemble a set of 944 genes associated with the disease based on the GWAS data set and Alzheimer's-related PPI data set collected in HENA. Selecting a set of genes that is clearly not associated with the Alzheimer's disease is even more challenging, and leads to the difficulty of defining the negative class. In this case study we approached this challenge by defining a negative class as a set of 1572 essential non-disease genes described in the evolutionary study by Spataro et al. 105. The rest of the genes present in HENA are labeled as unknown. Heterogeneous graph as an input for the model: The HENA data set can be represented as a heterogeneous graph, with nodes representing genes, and edges representing multiple biological relations between the genes (see Fig.
Graph features for each node represent a combination of graph embeddings, generated by GraphSAGE  23  and proportions of Alzheimer's disease-associated genes in the first and second hop neighbourhood of each node. In this study we use an unsupervised version of GraphSAGE, where for each node we learn embeddings, i. e. low-dimensional vector representations for network nodes, based on both node features and the graph structure. Each node is then represented via a numeric vector of a specified fixed dimension (256 in our case) that captures the node properties in the graph in addition to the node features. Moreover, as we deal with a heterogeneous graph, we learn embeddings for each of the three sub-graphs, where a subgraph consists of edges of a particular Fig. 5  Comparison of HENA data set with public databases.
The number of intersecting interactions between HENA, GeneMania and STRING are demonstrated respectively in, (a) PPI, (b) co-expression and (c) genetic interactions. The line between the two data sets in the figure meta-data displayed under x-axes represents the intersecting sets. In the case of a data set intersecting with itself, a single dot is displayed. type, i. e. co-expression, PPI, epistasis. Therefore, the dimensionality of the resulting embedding vector for each node is 256 × 3 as we concatenate embeddings from sub-graphs for each edge type. A third set of features all features consists of the combined biological features and graph-related features. Nodes with these three sets of features are then used as an input for the classification model HinSAGE. The performance of the model on three different feature sets is described in Table  6.
Graph structure helps to capture complex relationships. For the exploratory analysis we selected genes suggested as candidates for an association with the Alzheimer's disease by the algorithms. Here we performed surveyed the existing body of research about the suggested genes. For this purpose we have created a list of genes, shown to have a strong association with the disease, from the recent publications. These publications include GWAS  [109] [110] [111]  and genome-wide association study by proxy (GWAX) studies  112  , list of disease-specific autoantibodies in human sera  113  , list of genes reported to be associated in Alzheimer's disease downloaded from the MalaCards database  114  , and the results of integrative transcriptome analysis by Raj et al. 115. This combined list resulted in 169 nodes present in the result of the classification.
acknowledgements We would like to acknowledge the funding support from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement No. 305299. We also give thanks to the  Estonian Research Council  grants ( PSG59 ;  IUT34-4 ); the  European Regional Development Fund for CoE of Estonian ICT  research  EXCITE  project. Additionally, we thank  Yuriy Tyshetskiy  and  Kevin Jung  who provided insight and expertise that greatly assisted the development of the case study, and  Ivan Kuzmin  for assisting in solving non-trivial technical issues.
Code availability Data integration and analysis were performed using R language for statistical computing  95  version 3. 0. 2 (2013-05-16), R version 3. 3. 1 (2016-06-21), R version 3. 4. 2 (2017-09-18 ). The case study was built using Python 3. 6. The project repository is accessible via GitHub HENA repository and accompanied by DOI  116, 117.