phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | distributed vector representations of DNA k-mers— “The usage of k-mer representation has been a popular approach in analyzing long sequence of DNA fragments.” | |
| claim-type | method— “In this paper, we propose a novel method to train distributed representations of variable-length k-mers.” | |
| method — evidence | — “The two-layer neural network training method described in Section 2.3 was implemented using gensim framework (Řehůřek and Sojka, 2010).” | |
| transfer | from linguistics (natural language processing): the word2vec word-embedding model, applied to DNA k-mers— “Our method is based on the popular word embedding model word2vec, which is trained on a shallow two-layer neural network” | transfers[0] |
| mechanism | training word2vec (CBOW/skip-gram) on overlapping variable-length DNA k-mers— “we propose a novel method to train distributed representations of variable-length k-mers” | mechanisms[0] |
| builds-on | word2vec (from NLP/linguistics)— “Our method is based on the popular word embedding model word2vec” | claim_relations[0] |
| builds-on | word2vec (Mikolov et al., 2013a)— “Our method is based on the popular word embedding model word2vec, which is trained on a shallow two-layer neural network.” | claim_relations[1] |
| extends | BioVec (Asgari and Mofrad, 2015) and seq2vec (Kimothi et al., 2016)— “Although both techniques used a two-layer neural network to train their embedding, our technique is a generalization for variable-length k.” | claim_relations[2] |
| sense | context - AS PHYSICAL ADJACENCY ALONG THE SEQUENCE: the set of k-mers next to the target within a fragment, which is what the word means here, not linguistic co-occurrence— “Our dna2vec algorithm is trained by predicting the “context” surrounding a given targeted k-mer. The “context” is the set of adjacent k-mers surrounding the targeted k-mer” | senses[0] |
| sense | the distributional hypothesis - AS THE TRANSFERRED WARRANT: a 1954 claim about words in the same context having similar meaning, doing the justificatory work for a model of nucleotides— “the linguistic concept of distributional hypothesis, which states that words that appear in the same context have similar meaning” | senses[1] |
| sense | k-mer length - AS A RANDOM VARIABLE: the window size is drawn from a discrete uniform distribution for each position, so the same sequence yields different k-mers on different runs— “we sample k from the discrete uniform distribution Uniform(klow, khigh) to determine the size of each window” | senses[2] |
| control | a null distribution built by scoring pairs of RANDOM 8-mers, against which the real scores are read — held — rules out similarity scores that look high simply because any two short sequences share structure - the null is constructed data rather than a reshuffling of the observed data— “The null distribution is from computing the score between two random 8-mers.” | controls[0] |
| retracted | EVIDENCE CORRECTED (RE-GROUNDED) · k-mer —operationalised-as→ one-hot vector — RE-GROUNDED. The old span named only 'one-hot vector'. This sentence names both and states the encoding explicitly - 'of k-mer AS a…'. | concept_edges[0] |
| outcome (stated by the tagger, no quotation) | control — held | controls[0] |
| for which use | limitation — any similarity computed over one-hot k-mer encodings: the distance between any arbitrary pair is equidistant, even though ATGGC should be closer to ATGGG than CACGA | limitations[0] |
| for which use | limitation — the reported correlation between dna2vec cosine similarity and sequence similarity: the Needleman-Wunsch scores use a match score of 1, mismatch of 0 and gap penalty of 0, so the correlation is with that scoring and not with alignment in general Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[1] |
| for which use | limitation — the claim that dna2vec vectors carry biological meaning: the training objective is warranted by the distributional hypothesis about words appearing in the same context, which the paper says it is loosely inspired by Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[2] |
| replication | untested | |
| limitation | The standard encoding asserts that every pair of sequences is equally unlike every other, which is false of the sequences themselves. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[0] |
| limitation | Every similarity result in the paper is measured against one stipulated alignment scoring, including a gap penalty of zero. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[1] |
| limitation | The method's justification is a hypothesis about words, carried over to nucleotides, and the paper marks the inspiration as loose. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[2] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | dna2vec trains distributed representations of variable-length DNA k-mers, based on the word embedding model word2vec. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[0] |
| finding | Summing dna2vec vectors approximates nucleotide concatenation, and cosine similarity of the vectors correlates with Needleman-Wunsch alignment similarity. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[1] |
| quantity | 100 dimensions — dimensionality of the continuous dna2vec vector space Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.quantities[0] |
| quantity | two-layer — depth of the shallow neural network (as in word2vec) Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.quantities[1] |
| variable | DNA k-mer (independent) Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[0] |
| variable | cosine similarity of dna2vec vectors (dependent) Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[1] |
| definition | k-mer Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[0] |
| definition | one-hot vector Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| key-terms | k-mer, vector, embedding, word2vec, DNA, similarity, nucleotide, representation, sequence |
| concept | k-mer Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[1] |
| concept | distributed representation Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[2] |
| concept | one-hot vector Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | dna2vec Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | word2vec Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | Needleman-Wunsch Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| correlation r | as written: correlation coefficient Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[0] |
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