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  • 1.

    【2hr】Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

    包量

    机译 基于连体残差RCNN的多面蛋白质相互作用预测
    摘要：MotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.
  • 2.

    【2hr】Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

    包量

    机译 通过多任务稀疏典范相关分析确定渐进式成像遗传模式：ADNI队列的纵向研究
    摘要：MotivationIdentifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.
  • 3.

    【2hr】PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

    包量

    机译 精确：一种域适应方法，可将药物反应的预测因子从临床前模型转移至肿瘤
    摘要：MotivationCell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting.
  • 4.

    【2hr】ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra

    包量

    机译 自适应：学习DAta-dePendenT，简洁的分子载体，可从串联质谱中快速，准确地鉴定代谢物
    摘要：MotivationMetabolite identification is an important task in metabolomics to enhance the knowledge of biological systems. There have been a number of machine learning-based methods proposed for this task, which predict a chemical structure of a given spectrum through an intermediate (chemical structure) representation called molecular fingerprints. They usually have two steps: (i) predicting fingerprints from spectra; (ii) searching chemical compounds (in database) corresponding to the predicted fingerprints. Fingerprints are feature vectors, which are usually very large to cover all possible substructures and chemical properties, and therefore heavily redundant, in the sense of having many molecular (sub)structures irrelevant to the task, causing limited predictive performance and slow prediction.
  • 5.

    【2hr】SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

    包量

    机译 SCRIBER：准确和伙伴类型特异性预测蛋白序列中的蛋白结合残基
    摘要：MotivationAccurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.
  • 6.

    【2hr】Bayesian metabolic flux analysis reveals intracellular flux couplings

    包量

    机译 贝叶斯代谢通量分析显示细胞内通量耦合
    摘要：MotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.
  • 7.

    【2hr】Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data

    包量

    机译 加权弹性网用于无监督域自适应，并应用于DNA甲基化数据的年龄预测
    摘要：MotivationPredictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains.
  • 8.

    【2hr】FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes

    包量

    机译 FunDMDeep-m6A：功能性差异m6A甲基化基因的鉴定和优先排序
    摘要：MotivationAs the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive.
  • 9.

    【2hr】DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning

    包量

    机译 扩散：通过深度学习从序列和表达谱预测同工型功能
    摘要：MotivationAlternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc.
  • 10.

    【2hr】Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues

    包量

    机译 质谱离子图像的无监督分割表征了组织的形态
    摘要：MotivationMass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution.
  • 11.

    【2hr】Selfish: discovery of differential chromatin interactions via a self-similarity measure

    包量

    机译 自私：通过自相似度量发现差异性染色质相互作用
    摘要：MotivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.
  • 12.

    【2hr】Inference of clonal selection in cancer populations using single-cell sequencing data

    包量

    机译 使用单细胞测序数据推断癌症人群中的克隆选择
    摘要：SummaryIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer.
  • 13.

    【2hr】Building large updatable colored de Bruijn graphs via merging

    包量

    机译 通过合并构建大型可更新的彩色de Bruijn图
    摘要：MotivationThere exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed.
  • 14.

    【2hr】Deep learning with multimodal representation for pancancer prognosis prediction

    包量

    机译 具有多模式表示的深度学习用于癌症预后预测
    摘要：MotivationEstimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type—using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.
  • 15.

    【2hr】A divide-and-conquer method for scalable phylogenetic network inference from multilocus data

    包量

    机译 从多位点数据推断可扩展系统发生网络的分治法
    摘要：MotivationReticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes.
  • 16.

    【2hr】Learning a mixture of microbial networks using minorization–maximization

    包量

    机译 使用最小化-最大化学习混合微生物网络
    摘要：MotivationThe interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network.
  • 17.

    【2hr】Drug repositioning based on bounded nuclear norm regularization

    包量

    机译 基于有界核规范正则化的药物重新定位
    摘要：MotivationComputational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug–disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug–disease associations.
  • 18.

    【2hr】Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose–response experiments

    包量

    机译 增强药物发现过程：贝叶斯推理，用于剂量反应实验的分析和比较
    摘要：MotivationThe efficacy of a chemical compound is often tested through dose–response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt–Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose–response experiments.
  • 19.

    【2hr】Integrating read-based and population-based phasing for dense and accurate haplotyping of individual genomes

    包量

    机译 整合基于读取和基于群体的定相，以实现单个基因组的密集和准确单倍型
    • 作者：Vikas Bansal
    • 刊名：Bioinformatics
    • 2019年第14期
    摘要：MotivationReconstruction of haplotypes for human genomes is an important problem in medical and population genetics. Hi-C sequencing generates read pairs with long-range haplotype information that can be computationally assembled to generate chromosome-spanning haplotypes. However, the haplotypes have limited completeness and low accuracy. Haplotype information from population reference panels can potentially be used to improve the completeness and accuracy of Hi-C haplotyping.
  • 20.

    【2hr】Large scale microbiome profiling in the cloud

    包量

    机译 云中的大规模微生物组分析
    摘要：MotivationBacterial metagenomics profiling for metagenomic whole sequencing (mWGS) usually starts by aligning sequencing reads to a collection of reference genomes. Current profiling tools are designed to work against a small representative collection of genomes, and do not scale very well to larger reference genome collections. However, large reference genome collections are capable of providing a more complete and accurate profile of the bacterial population in a metagenomics dataset. In this paper, we discuss a scalable, efficient and affordable approach to this problem, bringing big data solutions within the reach of laboratories with modest resources.
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