The evolution of mass spectrometry-based proteomic technologies has advanced our understanding of the complex and dynamic nature of proteomes while concurrently revealing that no 'one-size-fits-all' proteomic strategy can be used to address all biological questions. Whereas some techniques, such as those for analyzing protein complexes, have matured and are broadly applied with great success, others, such as global quantitative protein expression profiling for biomarker discovery, are still confined to a few expert laboratories. In this Perspective, we attempt to distill the wide array of conceivable proteomic approaches into a compact canon of techniques suited to asking and answering specific types of biological questions. By discussing the relationship between the complexity of a biological sample and the difficulty of implementing the appropriate analysis approach, we contrast areas of proteomics broadly usable today with those that require significant technical and conceptual development. We hope to provide nonexperts with a guide for calibrating expectations of what can realistically be learned from a proteomics experiment and for gauging the planning and execution effort. We further provide a detailed supplement explaining the most common techniques in proteomics.

https://ccsb.stanford.edu/content/dam/sm/ccsb/documents/education/cbio243course/2010_Mallick_Proteomics_a_pragmatic_perspective.pdf

Proteomics: a pragmatic perspective

The systematic and quantitative molecular analysis of mutant organisms that has been pioneered by studies on mutant metabolomes and transcriptomes holds great promise for improving our understanding of how phenotypes emerge. Unfortunately, owing to the limitations of classical biochemical analysis, proteins have previously been excluded from such studies. Here we review how technical advances in mass spectrometry-based proteomics can be applied to measure changes in protein abundance, posttranslational modifications and protein-protein interactions in mutants at the scale of the proteome. We finally discuss examples that integrate proteomics data with genomic and phenomic information to build network-centred models, which provide a promising route for understanding how phenotypes emerge.

Applying mass spectrometry-based proteomics to genetics, genomics and network biology

Providing explanations for recommended actions is deemed one of the most important capabilities of expert systems (ES). There is little empirical evidence, however, that explanation facilities indeed influence user confidence in, and acceptance of, ES-based decisions and recommendations. This paper investigates the impact of ES explanations on changes in user beliefs toward ES-generated conclusions. Grounded on a theoretical model of argument, three alternative types of ES explanations A trace, justification, and strategy A were provided in a simulated diagnostic expert system performing auditing tasks. Twenty practicing auditors evaluated the outputs of the system in a laboratory setting. The results indicate that explanation facilities can make ES-generated advice more acceptable to users and that justification is the most effective type of explanation to bring about changes in user attitudes toward the system. These findings are expected to be generalizable to application domains that exhibit similar characteristics to those of auditing: domains in which decision making tends to be judgmental and yet highly consequential, and the correctness or validity of such decisions cannot be readily verified.

https://www.jstor.org/stable/pdfplus/249686.pdf

The impact of explanation facilities on user acceptance of expert systems advice

Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction.

https://dl.acm.org/doi/pdf/10.1145/3293882.3330574

DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888998001040

Knowledge level modelling: concepts and terminology

The organization of expert systems, a tutorial☆

The organization of expert systems: A prescriptive tutorial

Retrospective on “The organization of expert systems, a tutorial”

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The organization of expert systems, a tutorial☆

The organization of expert systems: A prescriptive tutorial

Retrospective on “The organization of expert systems, a tutorial”