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Biotech and life sciences
proteomics-ms
mass-spectrometry proteomics identification and quantification
MaxQuant, DIA-NN, Spectronaut
System
Techniques · 4
Primitives · 24
proteomics-ms
Isotope pattern deconvolution
physics · medium: Fit isotopic envelopes to mass spectrum peaks to assign charge and monoisotopic mass and collapse peaks to neutral masses
Peptide spectrum matching
physics · medium: Score observed MS/MS spectra against theoretical fragment spectra of digested database peptides to pick the best peptide match
Dynamic time warping
signal-processing · medium: DTW alignment of time series and trajectories of different speed
Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
Deflate
compression · low: DEFLATE/gzip: LZ77 + Huffman, the format behind HTTP gzip, zip and PNG
Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
›
Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
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Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
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Spline interpolation
numerics · low: Linear, bilinear/trilinear and cubic spline interpolation on grids and tables
Molecular fingerprints similarity
physics · low: Circular (ECFP/Morgan) bit fingerprints and Tanimoto similarity with popcount
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
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Peak detection
signal-processing · low: Robust peak and event detection in noisy signals with adaptive thresholds and refractory periods
Target decoy fdr
statistics · low: Estimate false discovery rate by searching a decoy database alongside targets and thresholding scores by decoy fraction (q-values)
XML Streaming Parser
storage · low: Tokenize XML into start, text and end events in constant memory, so a few fields can be extracted from huge, broken or hostile documents.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
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Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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+ 8 more primitives
System
Techniques · 4
Primitives · 24
proteomics-ms
Isotope pattern deconvolution
physics · medium: Fit isotopic envelopes to mass spectrum peaks to assign charge and monoisotopic mass and collapse peaks to neutral masses
Peptide spectrum matching
physics · medium: Score observed MS/MS spectra against theoretical fragment spectra of digested database peptides to pick the best peptide match
Dynamic time warping
signal-processing · medium: DTW alignment of time series and trajectories of different speed
Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
Deflate
compression · low: DEFLATE/gzip: LZ77 + Huffman, the format behind HTTP gzip, zip and PNG
Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
›
Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
›
Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
›
Spline interpolation
numerics · low: Linear, bilinear/trilinear and cubic spline interpolation on grids and tables
Molecular fingerprints similarity
physics · low: Circular (ECFP/Morgan) bit fingerprints and Tanimoto similarity with popcount
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
Peak detection
signal-processing · low: Robust peak and event detection in noisy signals with adaptive thresholds and refractory periods
Target decoy fdr
statistics · low: Estimate false discovery rate by searching a decoy database alongside targets and thresholding scores by decoy fraction (q-values)
XML Streaming Parser
storage · low: Tokenize XML into start, text and end events in constant memory, so a few fields can be extracted from huge, broken or hostile documents.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
›
BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
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Sorted-list intersection
search-sort · low: Intersect sorted id lists by merging, galloping from the short list into the long one, and skipping whole compressed blocks, shortest list first.
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d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
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L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Unicode text tokenization for search
search-sort · low: Turn multilingual text into index terms: segment words (UAX 29 style, CJK bigrams), normalize, case-fold and accent-fold, identically at index and query time.
›
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