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Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However, very few validated biomarkers are available to accurately predict survival benefit from postoperative chemotherapy. We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internally through cross-validation and externally using an independent cohort from The Cancer Genome Atlas (TCGA). We showed that CRCNet can accurately predict not only survival prognosis but also the treatment effect of adjuvant chemotherapy. The CRCNet identified high-risk subgroup benefits from adjuvant chemotherapy most and significant longer survival is observed among chemo-treated patients. Conversely, minimal chemotherapy benefit is observed in the CRCNet low- and medium-risk subgroups. Therefore, CRCNet can potentially be of great use in guiding treatments for Stage II/III CRC.

Most organisms exhibit various endogenous oscillating behaviors which provide crucial information as to how the internal biochemical processes are connected and regulated. Understanding the molecular mechanisms behind these oscillators requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated using an advanced MCMC which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis approach based on the intervention posterior. This approach measures the influence of individual parameters on the target process by utilizing the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, Neurospora crassa.

The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalisation. Today, the traditional statistical analyses approaches may seem questionable to practitioners in light of ubiquitous historical covariate information. Several methodological developments aim at incorporating historical information in the design and analysis of future clinical trials, most importantly Bayesian information borrowing, propensity score methods, stratification, and covariate adjustment. Recently, adjusting the analysis with respect to a prognostic score, which was obtained from some machine learning procedure applied to historical data, has been suggested and we study the potential of this approach for randomised clinical trials. In an idealised situation of a normal outcome in a two-arm trial with 1:1 allocation, we derive a simple sample size reduction formula as a function of two criteria characterising the prognostic score: (1) The coefficient of determination $R^2$ on historical data and (2) the correlation $\rho$ between the estimated and the true unknown prognostic scores. While maintaining the same power, the original total sample size $n$ planned for the unadjusted analysis reduces to $(1 - R^2 \rho^2) \times n$ in an adjusted analysis. Robustness in less ideal situations was assessed empirically. We conclude that there is potential for substantially more powerful or smaller trials, but only when prognostic scores can be accurately estimated.

Clinical studies often encounter with truncation-by-death problems, which may render the outcomes undefined. Statistical analysis based only on observed survivors may lead to biased results because the characters of survivors may differ greatly between treatment groups. Under the principal stratification framework, a meaningful causal parameter, the survivor average causal effect, in the always-survivor group can be defined. This causal parameter may not be identifiable in observational studies where the treatment assignment and the survival or outcome process are confounded by unmeasured features. In this paper, we propose a new method to deal with unmeasured confounding when the outcome is truncated by death. First, a new method is proposed to identify the heterogeneous conditional survivor average causal effect based on a substitutional variable under monotonicity. Second, under additional assumptions, the survivor average causal effect on the overall population is also identified. Furthermore, we consider estimation and inference for the conditional survivor average causal effect based on parametric and nonparametric methods with good asymptotic properties. Good finite-sample properties are demonstrated by simulation and sensitivity analysis. The proposed method is applied to investigate the effect of allogeneic stem cell transplantation types on leukemia relapse.

Consider two or more forecasters, each making a sequence of predictions for different events over time. We ask a relatively basic question: how might we compare these forecasters, either online or post-hoc, while avoiding unverifiable assumptions on how the forecasts or outcomes were generated? This work presents a novel and rigorous answer to this question. We design a sequential inference procedure for estimating the time-varying difference in forecast quality as measured by a relatively large class of proper scoring rules (bounded scores with a linear equivalent). The resulting confidence intervals are nonasymptotically valid, and can be continuously monitored to yield statistically valid comparisons at arbitrary data-dependent stopping times ("anytime-valid"); this is enabled by adapting variance-adaptive supermartingales, confidence sequences, and e-processes to our setting. Motivated by Shafer and Vovk's game-theoretic probability, our coverage guarantees are also distribution-free, in the sense that they make no distributional assumptions on the forecasts or outcomes. In contrast to a recent work by Henzi and Ziegel, our tools can sequentially test a weak null hypothesis about whether one forecaster outperforms another on average over time. We demonstrate their effectiveness by comparing forecasts on Major League Baseball (MLB) games and statistical postprocessing methods for ensemble weather forecasts.

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different learning objectives. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results on these tasks, and consistently outperforms baselines on these tasks.

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention thanks to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge -- a central topic in artificial intelligence -- has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-to-graph based models, and discuss implications for the use of external knowledge in solving the NLI problem. Our model achieves the new state-of-the-art performance on the NLI problem over the SciTail science questions dataset.

Person re-identification is being widely used in the forensic, and security and surveillance system, but person re-identification is a challenging task in real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of Gaussian distribution model on pixel features, which include color moments, color space values and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes, these local Gaussians for each region creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of earlier metric learning methods on this descriptor. The performance of the descriptor is evaluated on four public available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-arts, which demonstrate the superior performance.