Abstract: BAYSIC (BAYesian System for Integrated Combination) combines sets of genomic and other biological data features to optimize selected data feature attributes, for example, detecting genome variants including single nucleotide variants (SNVs) and small insertion/deletions in genomes. The present disclosure presents one possible embodiment employing BAYSIC to combine single nucleotide variants detected by several distinct variant calling methods into an integrated SNV call set that is more accurate than any single SNV calling method or any ad hoc method of combining call sets. BAYSIC is a, tested and validated method using unsupervised machine learning, employing Bayesian latent class inference to combine variant sets produced by different packages.

Abstract: The present disclosure presents methods, systems, and devices for dentifying new molecules directly from biological sequence information, with at least one of a desired bioactivity profile, functional attribute, biochemical reactivity, biological impact, pharmacological characteristic or therapeutic effect. The present disclosure further includes analyzing, at the processor, data features of biological sequence information and other data sources, including a feature-definition set by processing, using one or more bioinformatic techniques, computational algorithms, or methods of statistical machine learning, data sources relating to biological or chemical molecules, including biomolecules, including but not limited to peptides, having desired physical or chemical characteristics, bioactivities, functional attributes, biological impacts, pharmacologic properties or therapeutic effects.

Abstract: The present disclosure presents methods, systems, and devices for identifying new molecules directly from biological sequence information, with at least one of a desired bioactivity profile, functional attribute, biochemical reactivity, biological impact, pharmacological characteristic or therapeutic effect. The present disclosure further includes analyzing, at the processor, data features of biological sequence information and other data sources, including a feature-definition set by processing, using one or more bioinformatic techniques, computational algorithms, or methods of statistical machine learning, data sources relating to biological or chemical molecules, including biomolecules, including but not limited to peptides, having desired physical or chemical characteristics, bioactivities, functional attributes, biological impacts, pharmacologic properties or therapeutic effects.

Abstract: The present disclosure presents methods, systems, and devices for dentifying new molecules directly from biological sequence information, with at least one of a desired bioactivity profile, functional attribute, biochemical reactivity, biological impact, pharmacological characteristic or therapeutic effect. The present disclosure further includes analyzing, at the processor, data features of biological sequence information and other data sources, including a feature-definition set by processing, using one or more bioinformatic techniques, computational algorithms, or methods of statistical machine learning, data sources relating to biological or chemical molecules, including biomolecules, including but not limited to peptides, having desired physical or chemical characteristics, bioactivities, functional attributes, biological impacts, pharmacologic properties or therapeutic effects.

Abstract: BAYSIC (BAYesian System for Integrated Combination) combines sets of genomic and other biological data features to optimize selected data feature attributes, for example, detecting genome variants including single nucleotide variants (SNVs) and small insertion/deletions in genomes. The present disclosure presents one possible embodiment employing BAYSIC to combine single nucleotide variants detected by several distinct variant calling methods into an integrated SNV call set that is more accurate than any single SNV calling method or any ad hoc method of combining call sets. BAYSIC is a, tested and validated method using unsupervised machine learning, employing Bayesian latent class inference to combine variant sets produced by different packages.

Abstract: The present disclosure presents methods, systems, and devices for dentifying new molecules directly from biological sequence information, with at least one of a desired bioactivity profile, functional attribute, biochemical reactivity, biological impact, pharmacological characteristic or therapeutic effect. The present disclosure further includes analyzing, at the processor, data features of biological sequence information and other data sources, including a feature-definition set by processing, using one or more bioinformatic techniques, computational algorithms, or methods of statistical machine learning, data sources relating to biological or chemical molecules, including biomolecules, including but not limited to peptides, having desired physical or chemical characteristics, bioactivities, functional attributes, biological impacts, pharmacologic properties or therapeutic effects.