Global Interactions) Download.zip ##VERIFIED##

Global Interactions) Download.zip ##VERIFIED##

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The global interaction dataset is based on the construction and analysis of ~23 million double mutants which identified 550,000 negative and 350,000 positive genetic interactions and covers ~90% of all yeast genes as either array and/or query mutants. The global genetic interaction dataset includes three different genetic interaction maps. First, 3,589 nonessential deletion query mutant strains were screened against the deletion mutant array covering 3,892 nonessential genes to generate a nonessential x nonessential (NxN) network. Second, 1,162 TS query mutant strains representing 804 essential genes were also screened against the nonessential deletion mutant array to generate an essential x nonessential (ExN) network. Finally, 2,241 nonessential deletion mutant query strains and 1,108 TS query mutant strains, corresponding to 795 essential genes, were crossed to an array of 792 TS strains, spanning 561 unique essential genes, to generate an expanded ExN network and an essential x essential (ExE) network. The data can be downloaded from the links below. Note that we continue to map genetic interactions for remaining gene pairs not represented in this dataset and we will update the data and networks as new interactions are generated.

What is Governance? Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.

There are many ways to expand your global awareness and get involved with Global Interactions. Contributions (both cash and goods) help to further our programs. Volunteering for special projects, serving as an intern, and partnering with international schools or organizations can create lasting relationships with international counterparts.

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Global Interactions welcomes individuals who wish to volunteer or intern with the organization to learn and grow in a global society. Please review the Staff/Volunteer Agreement and submit it to [email protected] We will get back with you as soon as possible.

GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

The GloVe model is trained on the non-zero entries of a global word-word co-occurrence matrix, which tabulates how frequently words co-occur with one another in a given corpus. Populating this matrix requires a single pass through the entire corpus to collect the statistics. For large corpora, this pass can be computationally expensive, but it is a one-time up-front cost. Subsequent training iterations are much faster because the number of non-zero matrix entries is typically much smaller than the total number of words in the corpus.

The horizontal bands result from the fact that the multiplicative interactions in the model occur component-wise. While there are additive interactions resulting from a dot product, in general there is little room for the individual dimensions to cross-pollinate.

The physical associations generated by chromatin contacts are a critical factor to regulate and determine gene-expression patterns [1,2,3,4]. Functional chromatin contacts can form across a wide range of genomic distances within a chromosome (cis) or across chromosomes (trans). Although trans contacts are non-random [5] and there is evidence of trans-regulatory interactions [6, 7], studying the functional role of these interactions is difficult due to the high sparsity of available chromatin contact maps in trans.

To overcome the sequencing-depth barrier, targeted 3C-based techniques such as ChiA-PET [11] and Capture-Hi-C [12] are widely used to obtain high-resolution contact maps for specific proteins or selected loci respectively. Alternatively, several in silico methods have taken the advantage of existing limited-resolution contact maps to either generate higher resolution maps using machine learning approaches [13,14,15,16] and/or detect statistically significant interactions by background fitting [10, 17]. However, with a few exceptions [18, 19], most of the available methods are only tested to enhance cis interactions because longer range interactions are essentially unavailable within any given data set.

The Hi-C matrix in cis has high density of contacts at bins near the diagonal and the contact density decreases exponentially as the distance between the bins increases so that even Hi-C networks with higher contact density on average will be highly sparse at distant bins. This makes it difficult to capture functional contacts between distant gene pairs from a Hi-C matrix. Hence, we evaluated the contact coexpression of individual Hi-C networks and meta-Hi-C networks at various linear distance thresholds in cis. We find that for long-range contacts (minimum distance between gene pairs> 600 KB) the additional sequencing depth of meta-Hi-C networks when compared to individual Hi-C networks fully converts into additional performance (Fig. 2F). However, for both individual networks and meta-Hi-C network, the performance decreases in the absence of short-range contacts. This could be due to a higher number of short-range regulatory interactions or due to the similarity of the chromatin environment for nearby genes.

We compared TAD coexpression (defined only in cis), compartment coexpression, and subcompartment coexpression with meta-Hi-C contact coexpression at several resolutions. We used two different methods for calling compartments: an older PCA-based method Liberman-Aiden et al. [8] and a comparatively recent method Calder [23]. In cis, we find that compartment and subcompartment coexpression is comparable to or better than contact coexpression while TAD coexpression is lower than compartment at up to 10-KB resolution (Additional file 1: Fig. S3). TADs are often considered functional genomic units and genes within the same TADs tend to be coexpressed [25]. However, unlike compartment and contact coexpression, TAD coexpression does not capture long-range interactions (average TAD size is smaller than 1 MB). This likely explains the non-random yet low performance of TAD coexpression (AUC 0.55). We also evaluated the conservation of TADs and boundaries across all individual Hi-C matrices (Additional file 1: Fig. S4). The number of TADs conserved across experiments decreases relatively rapidly and we did not find any TAD which was conserved across all the experiments. In trans, we find that compartment coexpression and subcompartment coexpression performances are lower than contact coexpression performance, suggesting other trans interactions contribute (Fig. 2G).

Within the Hi-C analysis, and even outside of it, aggregation of data is well appreciated to be a useful strategy. Reproducible biological replicates within the same study are often combined to increase the density of Hi-C data thereby capturing more interactions [10, 33]. Our approach can be thought of as the most extreme version of this idea, combining experiments as broadly as possible to capture statistical relationships that are common. This is most useful if the depth is a major limitation, as in trans contacts, as it comes with the cost of a loss of condition-specificity. Thus, the route forward for the field as a whole will doubtless involve both improved specificity, integration, and interpretive methods.

Recently, Vision Transformer and its variants have shown great promise on various computer vision tasks. The ability to capture local and global visual dependencies through self-attention is the key to its success. But it also brings challenges due to quadratic computational overhead, especially for the high-resolution vision tasks(e.g., object detection). Many recent works have attempted to reduce the cost and improve model performance by applying either coarse-grained global attention or fine-grained local attention. However, both approaches cripple the modeling power of the original self-attention mechanism of multi-layer Transformers, leading to sub-optimal solutions. In this paper, we present focal attention, a new attention mechanism that incorporates both fine-grained local and coarse-grained global interactions. In this new mechanism, each token attends its closest surrounding tokens at the fine granularity and the tokens far away at a coarse granularity and thus can capture both short- and long-range visual dependencies efficiently and effectively. With focal attention, we propose a new variant of Vision Transformer models, called Focal Transformers, which achieve superior performance over the state-of-the-art (SoTA) Vision Transformers on a range of public image classification and object detection benchmarks. In particular, our Focal Transformer models with a moderate size of 51.1M and a large size of 89.8M achieve 83.6% and 84.0%Top-1 accuracy, respectively, on ImageNet classification at 224×224. When employed as the backbones, Focal Transformers achieve consistent and substantial improvements over the current SoTA Swin Transformers [44] across 6 different object detection methods. Our largest Focal Transformer yields58.7/59.0boxmAPs and50.9/51.3mask mAPs on COCO mini-val/test-dev, and55.4mIoU onADE20K for semantic segmentation, creating new SoTA on three of the most challenging computer vision tasks. 2b1af7f3a8