Machine Learning Experts Research Articles

Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation

Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.

A SURVEY OF BRAIN TUMOR DETECTION USING MACHINE LEARNING TECHNIQUES

Brain tumors are one of the most critical health concerns worldwide, requiring accurate and timely diagnosis for effective treatment planning. The advent of machine learning techniques has shown promising results in automating the detection of brain tumors, aiding medical professionals in making informed decisions. This paper presents a comprehensive review of the application of machine learning approaches for brain tumor detection. The review encompasses various machine learning algorithms and methodologies employed in brain tumor detection and the challenges associated with brain tumor detection. The review concludes by summarizing the current state-of-the-art in machine learning-based brain tumor detection, identifying potential areas for improvement, and discussing future research directions. In conclusion, machine learning approaches have demonstrated significant potential in automating brain tumor detection, offering a valuable tool for radiologists and clinicians. However, further advancements and collaborations between machine learning experts and medical professionals are crucial to develop hybrid techniques and reliable models that can assist in early and accurate brain tumor diagnosis.

Version [formula omitted]- [AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

We report a new release of the self-explainable AutoML software AMLBID. The software package is a meta-learning based AutoML decision support system that assists machine learning(ML) experts and neophytes in building well performing ML pipelines and makes the outcomes transparent and interpretable. The release 2.0 of the software introduces an enhanced efficiency of algorithms recommendation. The performance improvement is mainly achieved by the integration of the AeKNN meta-model. This implementation makes datasets meta-features more informative and further improve the accuracy. The release 2.0 also introduces the standalone AMLBIDesc, a desktop version of the AMLBID software which makes the tool more accessible to non-expert users.

Low-Code Machine Learning Platforms: A Fastlane to Digitalization

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms can address. Results showed that automatic machine learning platforms can provide a fast track for organizations seeking the digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms can provide a viable option to many business cases and, henceforth, provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

BackgroundThirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients.MethodData used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).ResultsThe models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658).ConclusionDiscriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up.

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.

A Review paper on the Impact of Artificial Intelligence on the Job Market

Artificial intelligence (AI) has revolutionized various industries, and its impact on the job market is significant. While AI has the potential to automate repetitive and time-consuming tasks, it also presents new job opportunities that require expertise in data analysis, software engineering, and machine learning. The paper provides an overview of the current state of the job market and the challenges it faces, and explains how AI is transforming the job market, including its impact on job creation and destruction, skill requirements, and wage distribution. The paper analyzes the job market trends from 2019 to 2023 based on the latest research and reports, and discusses the implications of AI for the workforce and policymakers. The paper concludes with some recommendations for future research and policy interventions. It discusses the benefits of AI, such as increased productivity, efficiency, and accuracy, and highlights the challenges that AI poses, including the displacement of certain jobs and the need for retraining workers. The review paper also discusses how AI is impacting workplace safety and health, including both the potential benefits and the new hazards that AI presents. Finally, the review paper explores the policies and regulations needed to ensure that AI is used ethically and responsibly in the job market. Overall, this review paper aims to provide a comprehensive understanding of the impact of AI on the job market and its implications for workers, businesses, and society

Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals

We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future.

DeepWild: Application of the pose estimation tool DeepLabCut for behaviour tracking in wild chimpanzees and bonobos.

Studying animal behaviour allows us to understand how different species and individuals navigate their physical and social worlds. Video coding of behaviour is considered a gold standard: allowing researchers to extract rich nuanced behavioural datasets, validate their reliability, and for research to be replicated. However, in practice, videos are only useful if data can be efficiently extracted. Manually locating relevant footage in 10,000 s of hours is extremely time-consuming, as is the manual coding of animal behaviour, which requires extensive training to achieve reliability. Machine learning approaches are used to automate the recognition of patterns within data, considerably reducing the time taken to extract data and improving reliability. However, tracking visual information to recognise nuanced behaviour is a challenging problem and, to date, the tracking and pose-estimation tools used to detect behaviour are typically applied where the visual environment is highly controlled. Animal behaviour researchers are interested in applying these tools to the study of wild animals, but it is not clear to what extent doing so is currently possible, or which tools are most suited to particular problems. To address this gap in knowledge, we describe the new tools available in this rapidly evolving landscape, suggest guidance for tool selection, provide a worked demonstration of the use of machine learning to track movement in video data of wild apes, and make our base models available for use. We use a pose-estimation tool, DeepLabCut, to demonstrate successful training of two pilot models of an extremely challenging pose estimate and tracking problem: multi-animal wild forest-living chimpanzees and bonobos across behavioural contexts from hand-held video footage. With DeepWild we show that, without requiring specific expertise in machine learning, pose estimation and movement tracking of free-living wild primates in visually complex environments is an attainable goal for behavioural researchers.

Heart Disease Prediction using Machine Learning

Heart disease is a significant global health concern, accounting for a substantial number of deaths worldwide. Early prediction and detection of heart disease play a pivotal role in improving patient outcomes and reducing mortality rates. Machine learning techniques have demonstrated considerable potential in accurately predicting heart disease based on patient data. In this research paper, we propose a novel approach to heart disease prediction using machine learning algorithms, with a particular focus on creating a user-friendly graphical user interface (GUI) for enhanced accessibility and ease of use. The proposed approach leverages a diverse dataset encompassing demographic information, medical history, laboratory results, and diagnostic tests, providing a comprehensive view of a patient's health status. Multiple state-of-the-art machine learning algorithms, including logistic regression, support vector machines, random forests, and artificial neural networks, are employed to build robust prediction models. These models are trained, validated, and evaluated using appropriate performance metrics to ensure accuracy and reliability. To facilitate practical implementation, a user-friendly GUI is designed to provide an intuitive interface for healthcare professionals and individuals without extensive programming or machine learning expertise.

Explainable Activity Recognition for Smart Home Systems

Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output of an underlying activity recognition system. However, activity recognition systems may not be perfectly accurate, and therefore inconsistencies in smart home operations can lead users reliant on smart home predictions to wonder “Why did the smart home do that?” In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods (Local Interpretable Model-agnostic Explanations, SHapley Additive exPlanations (SHAP), Anchors) to generate natural language explanations that explain what about an activity led to the given classification. We evaluate our framework in the context of a commonly targeted smart home scenario: autonomous remote caregiver monitoring for individuals who are living alone or need assistance. Within the context of remote caregiver monitoring, we perform a two-step evaluation: (a) utilize Machine Learning experts to assess the sensibility of explanations and (b) recruit non-experts in two user remote caregiver monitoring scenarios, synchronous and asynchronous, to assess the effectiveness of explanations generated via our framework. Our results show that the XAI approach, SHAP, has a 92% success rate in generating sensible explanations. Moreover, in 83% of sampled scenarios users preferred natural language explanations over a simple activity label, underscoring the need for explainable activity recognition systems. Finally, we show that explanations generated by some XAI methods can lead users to lose confidence in the accuracy of the underlying activity recognition model, while others lead users to gain confidence. Taking all studied factors into consideration, we make a recommendation regarding which existing XAI method leads to the best performance in the domain of smart home automation and discuss a range of topics for future work to further improve explainable activity recognition.

Frontier of Understanding Earth's Dynamics

Frontier of Understanding Earth's Dynamics

The Next Generation of Drugs Will Be Enhanced by Machine Learning

Genetic Engineering & Biotechnology NewsVol. 43, No. 4 DepartmentsThe Next Generation of Drugs Will Be Enhanced by Machine LearningExperts from Charles River and Valo Health describe how artificial intelligence will change the drug discovery landscapeGrant Wishart and Guido LanzaGrant WishartGrant Wishart, PhD, is senior director, small molecule drug discovery and Logica lead at Charles River Laboratories. Website: www.criver.com. Guido Lanza is vice president of integrated research and Logica general manager at Valo Health. Website: www.valohealth.com.Search for more papers by this author and Guido LanzaGrant Wishart, PhD, is senior director, small molecule drug discovery and Logica lead at Charles River Laboratories. Website: www.criver.com. Guido Lanza is vice president of integrated research and Logica general manager at Valo Health. Website: www.valohealth.com.Search for more papers by this authorPublished Online:10 Apr 2023https://doi.org/10.1089/gen.43.04.06AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 43Issue 4Apr 2023 Information© 2023 by GEN PublishingTo cite this article:Grant Wishart and Guido Lanza.The Next Generation of Drugs Will Be Enhanced by Machine Learning.Genetic Engineering & Biotechnology News.Apr 2023.16-17, 19.http://doi.org/10.1089/gen.43.04.06Published in Volume: 43 Issue 4: April 10, 2023PDF download

Examining Engineers’ Lived Experiences Deploying Machine Learning Production Models: A Phenomenological Study

This qualitative phenomenological study investigated machine learning (ML) model deployment challenges during the ML lifecycle using the theoretical framework of the technology acceptance model (TAM). Researchers have designed several frameworks for understanding the ML lifecycle, but those frameworks remain untested, and many ML model deployments still fail. The study’s central research question asked, what challenges do organizations face when deploying ML models in production environments? The phenomenological research design identified users’ perceptions and lived experiences deploying ML models in production environments. Data were collected via semi structured interviews with 15 ML experts. The phenomenon from the interviews was described in textural, structural, and textural-structural descriptions of participants’ lived experiences.

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms.

(1) Background: Differential diagnosis using immunohistochemistry (IHC) panels is a crucial step in the pathological diagnosis of hematolymphoid neoplasms. In this study, we evaluated the prediction accuracy of the ImmunoGenius software using nationwide data to validate its clinical utility. (2) Methods: We collected pathologically confirmed lymphoid neoplasms and their corresponding IHC results from 25 major university hospitals in Korea between 2015 and 2016. We tested ImmunoGenius using these real IHC panel data and compared the precision hit rate with previously reported diagnoses. (3) Results: We enrolled 3052 cases of lymphoid neoplasms with an average of 8.3 IHC results. The precision hit rate was 84.5% for these cases, whereas it was 95.0% for 984 in-house cases. (4) Discussion: ImmunoGenius showed excellent results in most B-cell lymphomas and generally showed equivalent performance in T-cell lymphomas. The primary reasons for inaccurate precision were atypical IHC profiles of certain cases, lack of disease-specific markers, and overlapping IHC profiles of similar diseases. We verified that the machine-learning algorithm could be applied for diagnosis precision with a generally acceptable hit rate in a nationwide dataset. Clinical and histological features should also be taken into account for the proper use of this system in the decision-making process.

The future of employment revisited: how model selection affects digitization risks

The uniqueness of human labour is at question in times of smart technologies. As computing power and data available increases, the discussion on technological unemployment reawakens. Prominently, Frey and Osborne (Technol Forecast Soc Change 114:254–280, 2017) estimated that half of US employment must be considered exposed to computerization within the next 20 years; followed by a series of papers expanding the research with information on heterogeneous job-specific tasks within the same jobs diminishing digitization potentials to only smaller fractions of workers at high risk. The main contribution of our work is to show that the diversity of previous findings regarding the degree of digitization is additionally driven by model selection. For our case study, we consult experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.

Abstract P2-12-02: ClinicalomicsDB - Bridging the gap between clinical omics data and machine learning

Abstract BACKGROUND: Clinical trials are controlled patient studies aiming to objectively assess the effectiveness of treatment interventions. However, the average effectiveness observed at the group level does not directly apply to individual patients. Cancer clinical trials that include molecular profiling on the baseline tumor samples enable the discovery of treatment response-associated molecular features and the development of prediction models that help discriminate responders from non-responders in each treatment arm. Despite potential opportunities, there are many challenges associated with the predictive modeling of such data, as exemplified by the small sample size and large feature space. Advances in machine learning and artificial intelligence (AI) may provide new solutions to address these challenges. However, for machine learning and AI to have an impact, data needs to be carefully curated, high-quality, standardized, and easily accessible and understood by data scientists, who may not have the domain knowledge. METHODS: Here, we created a python package “ClinicalOmicsDB” to address the challenges of data accessibility and promote development and application of machine learning methods to omics data from clinical trial samples with treatment response information. The package makes data readily analyzable by data scientists so that they can develop, utilize, and optimize their algorithms for predicting treatment responses and discovering novel biomarkers. To promote a two-way dialogue, we have also developed several Jupyter Notebook tutorials for biologists or clinicians who wish to gain expertise in machine learning. Omics data from clinical studies are downloaded from Gene Expression Omnibus (GEO) and responses were determined based on clinical trial primary endpoints. Currently, the package has datasets from 22 breast cancer clinical trials, including a total of 5050 patients (Table 1). It will be continuously expanded to include additional trials for breast cancer and other cancer types. RESULTS: To evaluate package utility, we built machine learning models to predict neoadjuvant chemotherapy with four cycles of 5-fluorouracil/epirubicin/cyclophosphamide (FEC) followed by four cycles of docetaxel/capecitabine on US Oncology clinical trial 02-103 [GSE42822]. The best performing model was the Random Forest Classifier model, which had an AUC of 0.817. To determine the generalizability of machine learning models established from the package, we trained a Random Forest Classifier model using the GSE25055 breast cancer dataset and apply the model to a different breast cancer dataset, GSE20194, which yielded an AUC of 0.648. These results suggest utilizing machine learning on clinical omics datasets can provide predictive and generalizable models that could be implemented in clinical settings for future breast cancer patients. CONCLUSION: We are expanding the database for data scientists, biologists, and clinicians to practice novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches for future patients. As more people add new data to the package, we will work towards improving pharmaceutical and private companies’ clinical trial data sharing policies and practices to promote data sharing. Table 1. Available breast cancer datasets in ClinicalOmicsDB The current database has 22 breast cancer clinical trials with 5050 total patients. Therapy shows various cytotoxic and/or targeted treatments utilized in a clinical trial. The database will be continuously expanded to include additional trials for breast cancer and other cancer types. Citation Format: Chang In Moon, Byron Jia, Bing Zhang. ClinicalomicsDB - Bridging the gap between clinical omics data and machine learning [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-12-02.

Impact of expenditures and corporate philanthropy disclosure on company value

PurposeThe paper aims to estimate how corporate philanthropy expenditures and corporate philanthropy disclosure (in general and in different spheres) affect investment attractiveness of Russian companies.Design/methodology/approachTo assess the degree of corporate philanthropy disclosure the authors compiled lexicons based on a set of techniques: text and frequency analysis, correlations, principal component analysis. To adjust the existing classifications of corporate philanthropic activities to the Russian market the authors employed expert analysis. The empirical research base includes 83 Russian publicly traded companies for the period 2013–2019. To estimate the impact of indicators of corporate philanthropy disclosure on company's investment attractiveness the authors utilized panel data regression and random forest algorithm.FindingsWe compiled 2 Russian lexicons: one on general issues of corporate philanthropy and another one on philanthropic activities in various spheres (sports and healthcare; support for certain groups of people; social infrastructure; children protection and youth policy; culture, education and science). 2. The paper observes that the disclosure of non-financial data including that related to general issues of corporate philanthropy as well as to different spheres affects the market capitalization of the largest Russian companies. The results of regression analysis suggest that disclosure of altruism-driven philanthropic activities (such as corporate philanthropy in the sphere of culture, education and science) has a lesser impact on company's investment attractiveness than that of activities driven by business-related motives (sports and healthcare, children protection and youth policy).Research limitations/implicationsOur findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the authors’ compiled lexicons.Originality/valueThe study aims to expand the domains of signaling and agency theories. First, this subject has not been widely examined in terms of emerging markets, the authors’ study is the first to focus on the Russian market. Secondly, the majority of scholars use text analysis to examine not only the impact of charitable donations but also the effect of corporate philanthropy disclosure. Thirdly, the authors provided the authors’ own lexicon of corporate philanthropy disclosure based on machine learning technique and expert analysis. Fourthly, to estimate the impact of corporate philanthropy on company's investment attractiveness the authors used the original approach based on combination of linear (regression), and non-linear methods (permutation importance. The authors’ findings extend the theoretical concept of Peterson et al. (2021): corporate philanthropy is viewed as the company strategy to reinforce its reputation, it helps to establish more efficient relationships with stakeholders which, in its turn, results in the increased business value.

Representation of an Interdisciplinary Approach for Evaluation of Precision of a Neurocognitive Screening Bundle: A Proposal

Neurocognitive disorders encompass a range of conditions characterized by cognitive impairments, presenting significant challenges to individuals, families, and healthcare systems worldwide. Traditional paper-and-pencil assessments have limitations, including restricted access, limited frequency, and subjective interpretation. This research proposal aims to address these limitations by developing a reliable digital platform, a neurocognitive screening bundle, for year-round monitoring of neurocognitive symptom domains. The proposed research project adopts an interdisciplinary approach by integrating knowledge and expertise from the fields of neurology, psychology, cognitive neuroscience, and computer science. Neurology provides clinical context and expertise in understanding neurocognitive symptom domains, ensuring that the chosen screening bundle aligns with clinical needs. Psychology contributes insights into human behavior and cognitive processes, aiding in the selection, adaptation, or development of assessments targeting specific neurocognitive functions. Additionally, psychologists contribute expertise in data analysis and interpretation, enabling the identification of meaningful patterns within the collected data. Cognitive neuroscience offers a deeper understanding of the underlying neural mechanisms associated with neurocognitive symptom domains. By identifying the neural networks, brain regions, and cognitive processes involved in specific cognitive functions, cognitive neuroscientists bridge the gap between cognitive assessment and neurobiological underpinnings, aiding in the selection and interpretation of neurocognitive measures. Computer science plays a pivotal role in developing and evaluating the digital platform for neurocognitive screening. Computer scientists leverage their expertise in software development, data analysis, and machine learning to design algorithms for data processing, ensure data security and privacy, and develop user-friendly interfaces for assessments. By integrating technical skills with knowledge from other disciplines, computer scientists enhance the accuracy, reliability, and usability of the digital platform. This interdisciplinary research project aims to provide a comprehensive, accessible, and reliable tool for year-round monitoring of neurocognitive symptom domains, surpassing the limitations of traditional assessment methods. By leveraging the expertise from neurology, psychology, cognitive neuroscience, and computer science, this study aims to advance the field and improve clinical outcomes for individuals with neurocognitive disorders. The findings of this research project have the potential to revolutionize neurocognitive assessments, leading to more effective diagnosis and monitoring of these challenging conditions.

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.