All Stories

  1. Tabular implicit deep neural networks ensembles for the prediction of pathogenic genetic variants in Mendelian diseases
  2. MiRInter-Trans: a Transformer-Based Framework for microRNA Interaction Prediction
  3. A Transformer-Based Model to Predict Micro RNA Interactions
  4. Modular Deep Neural Networks with Residual Connections for Predicting the Pathogenicity of Genetic Variants in Non Coding Genomic Regions
  5. AI methods and biologically informed data curation enable accurate RNA m5C prediction
  6. Replacing non-biomedical concepts improves embedding of biomedical concepts
  7. miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources
  8. miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources
  9. RNA Knowledge-Graph analysis through homogeneous embedding methods
  10. Fine-tuning of conditional Transformers improves in silico enzyme prediction and generation
  11. RNA knowledge-graph analysis through homogeneous embedding methods
  12. Leveraging generative AI to assist biocuration of medical actions for rare disease
  13. Fine-tuning of conditional Transformers for the generation of functionally characterized enzymes
  14. An ontology-based knowledge graph for representing interactions involving RNA molecules
  15. Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools
  16. Replacing non-biomedical concepts improves embedding of biomedical concepts
  17. Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests
  18. Association of post-COVID phenotypic manifestations with new-onset psychiatric disease
  19. An open source knowledge graph ecosystem for the life sciences
  20. Exploring the similarity between genetic diseases improves their differential diagnosis and the understanding of their etiology
  21. Intrinsic-Dimension analysis for guiding dimensionality reduction and data-fusion in multi-omics data processing
  22. Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning
  23. The promises of large language models for protein design and modeling
  24. Predictive models of long COVID
  25. Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests
  26. A software resource for large graph processing and analysis
  27. GRAPE for fast and scalable graph processing and random-walk-based embedding
  28. An expectation–maximization framework for comprehensive prediction of isoform-specific functions
  29. Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
  30. A Meta-Graph for the Construction of an RNA-Centered Knowledge Graph
  31. Degree-Normalization Improves Random-Walk-Based Embedding Accuracy in PPI Graphs
  32. Integration and Visual Analysis of Biomolecular Networks Through UNIPred-Web
  33. Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques
  34. Metformin is Associated with Reduced COVID-19 Severity in Patients with Prediabetes
  35. GraPE: fast and scalable Graph Processing and Embedding
  36. Automated image analysis to assess hygienic behaviour of honeybees
  37. ParSMURF-NG: A Machine Learning High Performance Computing System for the Analysis of Imbalanced Big Omics Data
  38. Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence
  39. HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction
  40. NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study
  41. Interpretable prioritization of splice variants in diagnostic next-generation sequencing
  42. Semi-automatic Column Type Inference for CSV Table Understanding
  43. Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools
  44. Protein function prediction as a graph-transduction game
  45. Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling
  46. Multitask Hopfield Networks
  47. Disease–Genes Must Guide Data Source Integration in the Gene Prioritization Process
  48. Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions
  49. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
  50. Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants
  51. COSNet: An R package for label prediction in unbalanced biological networks
  52. Within network learning on big graphs using secondary memory-based random walk kernels
  53. Multi-species protein function prediction
  54. An expanded evaluation of protein function prediction methods shows an improvement in accuracy
  55. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease
  56. RANKS: a flexible tool for node label ranking and classification in biological networks
  57. UNIPred: Unbalance-Aware Network Integration and Prediction of Protein Functions
  58. Learning node labels with multi-category Hopfield networks
  59. A Hierarchical Ensemble Method for DAG-Structured Taxonomies
  60. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods
  61. Hierarchical Ensemble Methods for Protein Function Prediction
  62. Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction
  63. GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology
  64. Network-Based Drug Ranking and Repositioning with Respect to DrugBank Therapeutic Categories
  65. Energy‐Efficient Resource Utilization in Cloud Computing
  66. A neural network algorithm for semi-supervised node label learning from unbalanced data
  67. A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering
  68. Regeneration-associated WNT Signaling Is Activated in Long-term Reconstituting AC133bright Acute Myeloid Leukemia Cells
  69. A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks
  70. Cancer module genes ranking using kernelized score functions
  71. Ensemble Methods
  72. Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories
  73. Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer
  74. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference
  75. A Mathematical Model for the Validation of Gene Selection Methods
  76. True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
  77. XML-Based Approaches for the Integration of Heterogeneous Bio-Molecular Data
  78. Ensembles in Machine Learning Applications
  79. COSNet: A Cost Sensitive Neural Network for Semi-supervised Learning in Graphs
  80. A Novel Ensemble Technique for Protein Subcellular Location Prediction
  81. Learning functional linkage networks with a cost-sensitive approach
  82. Dynamic multi-objective routing algorithm: a multi-objective routing algorithm for the simple hybrid routing protocol on wireless sensor networks
  83. Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines
  84. An Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction
  85. XML-based approaches for the integration of heterogeneous bio-molecular data
  86. Classification of co-expressed genes from DNA regulatory regions
  87. Computational intelligence and machine learning in bioinformatics
  88. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
  89. Applications of Supervised and Unsupervised Ensemble Methods
  90. True Path Rule Hierarchical Ensembles
  91. Ensemble Based Data Fusion for Gene Function Prediction
  92. A stability-based algorithm to validate hierarchical clusters of genes
  93. Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data
  94. Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources
  95. Classification of DNA microarray data with Random Projection Ensembles of Polynomial SVMs
  96. Comparing early and late data fusion methods for gene function prediction
  97. Dataset complexity can help to generate accurate ensembles of k-nearest neighbors
  98. Discovering multi–level structures in bio-molecular data through the Bernstein inequality
  99. HCGene: a software tool to support the hierarchical classification of genes
  100. Supervised and Unsupervised Ensemble Methods and their Applications
  101. Ensemble Clustering with a Fuzzy Approach
  102. Gene expression modeling through positive boolean functions
  103. Model order selection for bio-molecular data clustering
  104. Discovering Significant Structures in Clustered Bio-molecular Data Through the Bernstein Inequality
  105. Mosclust: a software library for discovering significant structures in bio-molecular data
  106. Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
  107. Characterization of lung tumor subtypes through gene expression cluster validity assessment
  108. Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
  109. Biological Specifications for a Synthetic Gene Expression Data Generation Model
  110. Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data
  111. An Experimental Bias-Variance Analysis of SVM Ensembles Based on Resampling Techniques
  112. Support vector machines for candidate nodules classification
  113. Bio-molecular cancer prediction with random subspace ensembles of support vector machines
  114. Lung nodules detection and classification
  115. An experimental analysis of the dependence among codeword bit errors in ECOC learning machines
  116. Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines
  117. Cancer recognition with bagged ensembles of support vector machines
  118. Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias–Variance Analysis
  119. Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines
  120. An Application of Low Bias Bagged SVMs to the Classification of Heterogeneous Malignant Tissues
  121. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles
  122. NEURObjects: an object-oriented library for neural network development
  123. Ensembles of Learning Machines
  124. Boosting and Classification of Electronic Nose Data
  125. Bias—Variance Analysis and Ensembles of SVM
  126. Decompositive classification models for electronic noses
  127. Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
  128. Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
  129. Feature Selection Combined with Random Subspace Ensemble for Gene Expression Based Diagnosis of Malignancies
  130. Fuzzy Ensemble Clustering for DNA Microarray Data Analysis
  131. An Algorithm to Assess the Reliability of Hierarchical Clusters in Gene Expression Data
  132. Data Integration Issues and Opportunities in Biological XML Data Management
  133. Bagged ensembles of Support Vector Machines for gene expression data analysis
  134. Random projections for assessing gene expression cluster stability