Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study.
Ontology highlight
ABSTRACT:
SUBMITTER: Valsecchi C
PROVIDER: S-EPMC7997107 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models-both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilizes the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.
| S-EPMC10616290 | biostudies-literature
Project description:Owing to the great variety of distinct peptide encodings, working on a biomedical classification task at hand is challenging. Researchers have to determine encodings capable to represent underlying patterns as numerical input for the subsequent machine learning. A general guideline is lacking in the literature, thus, we present here the first large-scale comprehensive study to investigate the performance of a wide range of encodings on multiple datasets from different biomedical domains. For the sake of completeness, we added additional sequence- and structure-based encodings. In particular, we collected 50 biomedical datasets and defined a fixed parameter space for 48 encoding groups, leading to a total of 397 700 encoded datasets. Our results demonstrate that none of the encodings are superior for all biomedical domains. Nevertheless, some encodings often outperform others, thus reducing the initial encoding selection substantially. Our work offers researchers to objectively compare novel encodings to the state of the art. Our findings pave the way for a more sophisticated encoding optimization, for example, as part of automated machine learning pipelines. The work presented here is implemented as a large-scale, end-to-end workflow designed for easy reproducibility and extensibility. All standardized datasets and results are available for download to comply with FAIR standards.
| S-EPMC8140742 | biostudies-literature
Project description:Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically establish comprehensive associations between AMP sequences and structures through structural folds and to provide an easy access to view their relationships. 30 distinct AMP structural fold clusters with more than one structure are detected and about a thousand AMPs are associated with at least one structural fold cluster. According to ADAM, AMP structural folds are limited-AMPs only cover about 3% of the overall protein fold space.
| S-EPMC4426897 | biostudies-literature
Project description:MotivationMetagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.ResultsWe propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.Availability and implementationData and codes are available at http://cbio.ensmp.fr/largescalemetagenomicsContactpierre.mahe@biomerieux.comSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4896366 | biostudies-literature
Project description:Internet Use Disorder (IUD) affects numerous adolescents worldwide, and (Internet) Gaming Disorder, a specific subtype of IUD, has recently been included in DSM-5 and ICD-11. Epidemiological studies have identified prevalence rates up to 5.7% among adolescents in Germany. However, little is known about the risk development during adolescence and its association to education. The aim of this study was to: (a) identify a clinically relevant latent profile in a large-scale high-school sample; (b) estimate prevalence rates of IUD for distinct age groups and (c) investigate associations to gender and education. N = 5387 adolescents out of 41 schools in Germany aged 11-21 were assessed using the Compulsive Internet Use Scale (CIUS). Latent profile analyses showed five profile groups with differences in CIUS response pattern, age and school type. IUD was found in 6.1% and high-risk Internet use in 13.9% of the total sample. Two peaks were found in prevalence rates indicating the highest risk of IUD in age groups 15-16 and 19-21. Prevalence did not differ significantly between boys and girls. High-level education schools showed the lowest (4.9%) and vocational secondary schools the highest prevalence rate (7.8%). The differences between school types could not be explained by academic level.
| S-EPMC5923775 | biostudies-literature
Project description:In 2022, two novel classification systems for myelodysplastic syndromes/neoplasms (MDS) have been proposed: the International Consensus Classification (ICC) and the 2022 World Health Organization (WHO-2022) classification. These two contemporary systems exhibit numerous shared features but also diverge significantly in terminology and the definition of new entities. Thus, we retrospectively validated the ICC and WHO-2022 classification and found that both systems promoted efficient segregation of this heterogeneous disease. After examining the distinction between the two systems, we showed that a peripheral blood blast percentage ≥ 5% indicates adverse survival. Identifying MDS/acute myeloid leukemia with MDS-related gene mutations or cytogenetic abnormalities helps differentiate survival outcomes. In MDS, not otherwise specified patients, those diagnosed with hypoplastic MDS and single lineage dysplasia displayed a trend of superior survival compared to other low-risk MDS patients. Furthermore, the impact of bone marrow fibrosis on survival was less pronounced within the ICC framework. Allogeneic transplantation appears to improve outcomes for patients diagnosed with MDS with excess blasts in the ICC. Therefore, we proposed an integrated system that may lead to the accurate diagnosis and advancement of future research for MDS. Prospective studies are warranted to validate this refined classification.
| S-EPMC11004131 | biostudies-literature
Project description:The analysis and identification of different attributes of produce such as taxonomy, vendor, and organic nature is vital to verifying product authenticity in a distribution network. Though a variety of analysis techniques have been studied in the past, we present a novel data-centric approach to classifying produce attributes. We employed visible and near infrared (NIR) spectroscopy on over 75,000 samples across several fruit and vegetable varieties. This yielded 0.90-0.98 and 0.98-0.99 classification accuracies for taxonomy and farmer classes, respectively. The most significant factors in the visible spectrum were variations in the produce color due to chlorophyll and anthocyanins. In the infrared spectrum, we observed that the varying water and sugar content levels were critical to obtaining high classification accuracies. High quality spectral data along with an optimal tuning of hyperparameters in the support vector machine (SVM) was also key to achieving high classification accuracies. In addition to demonstrating exceptional accuracies on test data, we explored insights behind the classifications, and identified the highest performing approaches using cross validation. We presented data collection guidelines, experimental design parameters, and machine learning optimization parameters for the replication of studies involving large sample sizes.
| S-EPMC5869718 | biostudies-literature
Project description:Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to novel inputs. Up to this point, there has been little attention to exploring the relationship between the two approaches and directly comparing performance on large-scale datasets that have more than a few dozen categories. Using the ImageNet ILSVRC-2012 large-scale classification dataset, we identify novel combinations of regularization and specialized inference methods that perform best across multiple open set classification problems of increasing difficulty level. We find that input perturbation and temperature scaling yield significantly better performance on large-scale datasets than other inference methods tested, regardless of the feature space regularization strategy. Conversely, we find that improving performance with advanced regularization schemes during training yields better performance when baseline inference techniques are used; however, when advanced inference methods are used to detect open set classes, the utility of these combersome training paradigms is less evident.
| S-EPMC7473573 | biostudies-literature
Project description:The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.
| S-EPMC6519453 | biostudies-literature
Project description:Today, the base code of DNA is mostly determined through sequencing by synthesis as provided by the Illumina sequencers. Although highly accurate, resulting reads are short, making their analyses challenging. Recently, a new technology, single molecule real-time (SMRT) sequencing, was developed that could address these challenges, as it generates reads of several thousand bases. But, their broad application has been hampered by a high error rate. Therefore, hybrid approaches that use high-quality short reads to correct erroneous SMRT long reads have been developed. Still, current implementations have great demands on hardware, work only in well-defined computing infrastructures and reject a substantial amount of reads. This limits their usability considerably, especially in the case of large sequencing projects.Here we present proovread, a hybrid correction pipeline for SMRT reads, which can be flexibly adapted on existing hardware and infrastructure from a laptop to a high-performance computing cluster. On genomic and transcriptomic test cases covering Escherichia coli, Arabidopsis thaliana and human, proovread achieved accuracies up to 99.9% and outperformed the existing hybrid correction programs. Furthermore, proovread-corrected sequences were longer and the throughput was higher. Thus, proovread combines the most accurate correction results with an excellent adaptability to the available hardware. It will therefore increase the applicability and value of SMRT sequencing.proovread is available at the following URL: http://proovread.bioapps.biozentrum.uni-wuerzburg.de.
| S-EPMC4609002 | biostudies-literature