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hts-screening

Biotech and life sciences | hts-screening — high-throughput and high-content screening analysis: plate maps, control-based normalisation (B-score), Z' assay QC, 4PL dose-response fits, hit calling, HCS image features and phenotypic profiles, compound registry link

Genedata Screener, Revvity Signals, CDD Vault

SystemTechniques · 22Primitives · 76 hts-screening DAG workflow executiondistributed · medium: Run a graph of dependent tasks by releasing each one when its inputs are done, longest chain first, and skip any task whose input hashes are unchanged.›Active learningmachine-learning · medium: Choosing which unlabeled examples to label next: uncertainty, margin, diversity and committee samplingContrastive learningmachine-learning · medium: Train an encoder so two views of the same thing score higher than every other pair in the batch; InfoNCE with a temperature, no labels needed.›Generalized linear models (GLMs)machine-learning · medium: Linear model for counts, rates, positive amounts and proportions: a linear score pushed through a link, fitted by iteratively reweighted least squares.›Image segmentationmachine-learning · medium: Pixel/instance segmentation networks and promptable segmentation (Mask R-CNN, SAM-style)Umap embeddingmachine-learning · medium: Nonlinear dimension reduction: build a fuzzy kNN graph and optimise a low-dimensional layout by cross-entropy with negative samplingLevenberg marquardtnumerics · medium: Damped Gauss-Newton nonlinear least squares for model calibrationk-means clusteringmachine-learning · low: Partition points into k clusters by alternating nearest-centroid assignment and centroid averaging; k-means++ seeding makes it dependable.›Convolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Bootstrap confidence intervalsstatistics · low: Resampling-based confidence intervals for arbitrary statisticsRobust statisticsstatistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.›Convolution image filtersgraphics · low: Separable 2D convolution kernels for blur, sharpen (unsharp mask) and edges with border handlingPCA and SVDmachine-learning · low: Find orthogonal directions of maximum variance by an SVD of the centred data; randomized SVD gets the top k components in a few passes.›Molecular fingerprints similarityphysics · low: Circular (ECFP/Morgan) bit fingerprints and Tanimoto similarity with popcountMedian polish normalizationstatistics · low: Robust removal of row/column bias in gridded measurements (median polish, B-score)Multiple testing correctionstatistics · low: Family-wise error and false discovery rate control (Bonferroni, Holm, Benjamini-Hochberg) over many testsColumnar file formatstorage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)Topological sortgraphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.›Content-addressed storagestorage · low: Store each blob under the hash of its bytes: immutable, deduplicated by construction, self-verifying on every read, and composable into Merkle trees.›Exponential backoff with jittersystems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.›Calibrationmachine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.›Embeddingsmachine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.›Data augmentationmachine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.›+ 15 more techniques+ 60 more primitives

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