Conservatism predicts aversion to consequential Artificial Intelligence.
Ontology highlight
ABSTRACT:
SUBMITTER: Castelo N
PROVIDER: S-EPMC8687590 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (≤0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness.
2012-12-10 | GSE42804 | GEO
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness. Female striped bass were sorted into groups (N=8 each) producing M-bM-^@M-^Xhigh qualityM-bM-^@M-^Y or M-bM-^@M-^Xlow qualityM-bM-^@M-^Y eggs (spawns) based upon the percentage of eggs bearing viable 4 h embryos. Spawns with >50% of eggs producing 4 h embryos were considered to be of high quality and spawns with <30% of eggs producing 4 h embryos were considered to be of low quality.
2012-12-10 | E-GEOD-42804 | biostudies-arrayexpress
Project description:PurposeTo develop and internally validate a machine-learning algorithm to reliably predict cost after anterior cruciate ligament reconstruction (ACLR).MethodsA retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective ACLR from 2015 to 2016. Features included in initial models consisted of patient characteristics (age, sex, insurance status, income, medical comorbidities as classified by the Clinical Classifications Software diagnosis code) as well as intraoperative variables (type of anesthesia and procedure-specific factors). Models were generated to predict total charges using 4 algorithms: random forest, extreme gradient boost, elastic net penalized regression, and support vector machines with radial kernels. Training was performed with 10-fold cross-validation followed by internal validation via 0.632 bootstrapping. Model discriminative performance was assessed by area under the receiver operating characteristic curve, calibration, and the Brier score. Decision curve analysis was performed to demonstrate the net benefit of using the final model in practice.ResultsIn total, 7,311 patients undergoing ambulatory ACLR were included. The random forest model demonstrated the best performance assessed via internal validation (area under the curve = 0.85), calibration, and the Brier score (0.208). Cost incurred was influenced by anesthesia type, operating room time, and number of chronic comorbidities. Decision curve analysis revealed a net benefit for use of the random forest model and the model was integrated into a web-based open-access application.ConclusionsThe random forest model predicted cost after ambulatory ACLR using a large, statewide database with good performance. The top variables found to predict increased charges were general anesthesia, operating room time, meniscal repair, self-pay insurance, patient neighborhood characteristics, and number of chronic conditions.Level of evidenceIII, retrospective cohort study.
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Project description:Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N?=?2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.
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Project description:Emotions have been proposed to inform risky decision-making through the influence of affective physiological responses on subjective value. The ability to perceive internal body states, or "interoception" may influence this relationship. Here, we examined whether interoception predicts participants' degree of loss aversion, which has been previously linked to choice-related arousal responses. Participants performed both a heartbeat-detection task indexing interoception and a risky monetary decision-making task, from which loss aversion, risk attitudes and choice consistency were parametrically measured. Interoceptive ability correlated selectively with loss aversion and was unrelated to the other value parameters. This finding suggests that specific and separable component processes underlying valuation are shaped not only by our physiological responses, as shown in previous findings, but also by our interoceptive access to such signals.
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Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description:The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex patterns from different radiological imaging modalities, many of which even achieve comparable performance to human decision-making in recent applications. In this chapter, we review several AI applications in radiology for different anatomies: chest, abdomen, pelvis, as well as general lesion detection/identification that is not limited to specific anatomies. For each anatomy site, we focus on introducing the tasks of detection, segmentation, and classification with an emphasis on describing the technology development pathway with the aim of providing the reader with an understanding of what AI can do in radiology and what still needs to be done for AI to better fit in radiology. Combining with our own research experience of AI in medicine, we elaborate how AI can enrich knowledge discovery, understanding, and decision-making in radiology, rather than replacing the radiologist.
| S-EPMC7484814 | biostudies-literature
Project description:The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes. Collaborations between biomedical and AI researchers will be highlighted in order to describe initial successes with an eye toward the future.
| S-EPMC6356893 | biostudies-other
Project description:Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.
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