Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps). A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal. In this paper, we address this gap by examining the extent to which current models comprehend the essentiality of step events in relation to a goal event. Cognitive studies suggest that such capability enables machines to emulate human commonsense reasoning about preconditions and necessary efforts of everyday tasks. We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow, with steps manually annotated for their essentiality concerning the goal by experts. The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality. However, after evaluating multiple statistical and largescale pre-trained language models, we find that existing approaches considerably underperform compared to humans. This observation highlights the need for further exploration into this critical and challenging task. The dataset and code are available at http://cogcomp.org/page/publication_view/1023.

Gene essentiality refers to the degree to which a gene is necessary for the survival and reproductive efficacy of a living organism. Although the essentiality of non-coding genes has been documented, there are still aspects of non-coding genes' essentiality that are unknown to us. For example, We do not know the contribution of sequence features and network spatial features to essentiality. As a consequence, in this work, we propose DeepHEN that could answer the above question. By buidling a new lncRNA-proteion-protein network and utilizing both representation learning and graph neural network, we successfully build our DeepHEN models that could predict the essentiality of lncRNA genes. Compared to other methods for predicting the essentiality of lncRNA genes, our DeepHEN model not only tells whether sequence features or network spatial features have a greater influence on essentiality but also addresses the overfitting issue of those methods caused by the low number of essential lncRNA genes, as evidenced by the results of enrichment analysis. Keywords: sample, graph neural network, representation learing, lncRNA-protein-protein network, essential non-coding genes INTORDUCTION Gene essentiality refers to the degree to which a gene is necessary for the survival and reproductive success of a living system. Genes that are indispensable in fulfilling these functions are classified as essential genes[1]. The concept of gene essentiality is dynamic and influenced by the specific context in which it is assessed.

Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for gene essentiality prediction. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach implicitly assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large changes in cell physiology to meet other objectives for survival. Here we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes.

Neoantigens play a key role in the recognition of tumour cells by T cells; however, only a small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which neoantigens are recognized by which T-cell receptors (TCRs). Researchers built a transfer learning-based model named the pMHC–TCR binding prediction network (pMTnet) to predict TCR binding specificities of the neoantigens--and T cell antigens in general--presented by class I major histocompatibility complexes. Altered transcription is a cardinal feature of acute myeloid leukemia (AML); however, exactly how mutations synergize to remodel the epigenetic landscape and rewire three-dimensional DNA topology is unknown. Here, researchers apply an integrated genomic approach to a murine allelic series that models the two most common mutations in AML: Flt3-ITD and Npm1c. High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research.