All Stories

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