If machines can learn, who needs scientists?
Ontology highlight
ABSTRACT:
SUBMITTER: Hoch JC
PROVIDER: S-EPMC6941139 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
If machines can learn, who needs scientists?
Journal of magnetic resonance (San Diego, Calif. : 1997) 20190716
Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existence of numerous public NMR data repositories. Other challenges include the problem of interpreting the results of a machine learning algorithm, and incorporating machine learning into hypothesis-driven research. This perspective imagines the potential ...[more]
Similar Datasets
Project description:Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
| S-EPMC6411769 | biostudies-literature
Project description:The inauguration of President Trump in the United States led to the active restriction of science communication from federal agencies, resulting in the creation of many unofficial "alt" Twitter accounts to maintain communication. Alt accounts had many followers (e.g., 15 accounts had > 100,000) and received a large amount of media attention, making them ideal for better understanding how differences in messaging can affect public engagement with science on microblogging platforms. We analyzed tweets produced by alt and corresponding official agency accounts to compare the two groups and determine if specific features of a tweet made them more likely to be retweeted or liked to help the average scientist potentially reach a broader audience on Twitter. We found adding links, images, hashtags, and mentions, as well as expressing angry and annoying sentiments all increased retweets and likes. Evidence-based terms such as "peer-review" had high retweet rates but linking directly to peer-reviewed publications decreased attention compared to popular science websites. Word choice and attention did not reflect official or alt account types, indicating topic is more important than source. The number of tweets generated and attention received by alt accounts has decreased since their creation, demonstrating the importance of timeliness in science communication on social media. Together our results show potential pathways for scientists to increase efficacy in Twitter communications.
| S-EPMC8570174 | biostudies-literature
Project description:MotivationContinuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).ResultsWe explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.
| S-EPMC9188262 | biostudies-literature
Project description:BackgroundLong-term health consequences following acute SARS-CoV-2 infection, referred to as post-COVID-19 condition or Long COVID, are increasing, with population-based prevalence estimates for adults at around 20%. Persons affected by Long COVID report various health problems, yet evidence to guide clinical decision making remains scarce.ObjectiveThe present study aimed to identify Long COVID research priorities using a citizen science approach and solely considering the needs of those affected.MethodsThis citizen science study followed an iterative process of patient needs identification, evaluation and prioritisation. A Long COVID Citizen Science Board (21 persons with Long COVID, and seven with myalgic encephalomyelitis/chronic fatigue syndrome) and a Long COVID Working Group (25 persons with Long COVID, four patients with myalgic encephalomyelitis/chronic fatigue syndrome and one relative) were formed. The study included four activities: three remote meetings and one online survey. First, Board members identified the needs and research questions. Second, Working Group members and persons affected by Long COVID (241 respondents, 85.5% with Long COVID, 14.5% with myalgic encephalomyelitis/chronic fatigue syndrome and 7.1% relatives) evaluated the research questions on a 1-5 Likert scale using an online survey. Then the Board gave feedback on this evaluation. Finally, Board members set the priorities for research through voting and discussion.ResultsSixty-eight research questions were generated by the Board and categorised into four research domains (medicine, healthcare services, socioeconomics and burden of disease) and 14 subcategories. Their average importance ratings were moderate to high and varied from 3.41 (standard deviation = 1.16) for sex-specific diagnostics to 4.86 (standard deviation = 0.41) for medical questions on treatment. Five topics were prioritised: "treatment, rehabilitation and chronic care management", "availability of interfaces for treatment continuity", "availability of healthcare structures", "awareness and knowledge among professionals" and "prevalence of Long COVID in children and adolescents".ConclusionsTo our knowledge, this is the first study developing a citizen-driven, explicitly patient-centred research agenda with persons affected by Long COVID, setting it apart from existing multi-stakeholder efforts. The identified priorities could guide future research and funding allocation. Our methodology establishes a framework for citizen-driven research agendas, suitable for transfer to other diseases.
| S-EPMC9046008 | biostudies-literature
Project description:A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.
| S-EPMC4021544 | biostudies-literature
Project description:BackgroundMost machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.ObjectivesThe aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.MethodsTwo random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.ResultsSimulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.ConclusionsRandom forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.
| S-EPMC3250568 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-04-28 | GSE173401 | GEO
Project description:Examination of relationship between chromatographic peak capacity and MS/MS scan rate.
2016-12-13 | PXD004126 | Pride
Project description:Riboswitches are structured allosteric RNA molecules that change conformation in response to a metabolite binding event, eventually triggering a regulatory response. Computational modelling of the structure of these molecules is complicated by a complex network of tertiary contacts, stabilized by the presence of their cognate metabolite. In this work, we focus on the aptamer domain of SAM-I riboswitches and show that Restricted Boltzmann machines (RBM), an unsupervised machine learning architecture, can capture intricate sequence dependencies induced by secondary and tertiary structure, as well as a switching mechanism between open and closed conformations. The RBM model is then used for the design of artificial allosteric SAM-I aptamers. To experimentally validate the functionality of the designed sequences, we resort to chemical probing (SHAPE-MaP), and develop a tailored analysis pipeline adequate for high-throughput tests of diverse homologous sequences. We probed a total of 476 RBM designed sequences in two experiments, showing between 20% and 40% divergence from any natural sequence, obtaining ≈ 30% success rate of correctly structured aptamers that undergo a structural switch in response to SAM.
2024-05-06 | GSE266263 | GEO
Project description:Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.
| S-EPMC5770455 | biostudies-literature