Sentence examples for tf I from inspiring English sources

It is based on computing BM25-tf score and idf component derived from the binary independence model that is well-known from the probabilistic theory in the information retrieval [19]: w i, j = BM 25 - tf i, j × idf i ∗ = tf i, j 0.5 + 1.5 × dl i dl avg + tf i, j × log N - df i + 0.5 df i + 0.5, (3).

where tf i,j means term frequency, N is a total number of documents in the collection, df i presents the document frequency, dl i document length and dl avg the average document length for the collection.

The importance of tf-idf increases proportionally to the occurrence of a word in the document and is offset by the frequency of the word in the collection of documents according to formula [5] w i, j = tf i, j × idf i = f i, j ∑ k f k, j × log N df i, (2).

SGR = ln W f - ln W i Time ( days ) × 100 FCR = Total amount of dry feed fed ( g ) Total wet weight gain of fish ( g ) Survival rate % = TF f TF i × 100 where TFf and TFi refer to the total final and initial number of fingerlings and fish, respectively.

In Ltu term weighting scheme, L factor expresses the logarithm of the term frequency, t factor the inverse document frequency, and u the length normalization factor as follows [21]: w i, j = L × t × u = ( log tf i, j + 1 ) × log N df i × 1 0.8 + 0.2 × dl i dl avg.

where fi,j is the number of occurrence of a term t i in a document d j and sum in the denominator of tf i,j component expresses the number of occurrences of all terms t i in d j.

Sixty two of these tissue-type TF-TF interaction networks have a linear relationship between TFs with 1 to 4 internal TFs (i.e. TF connecting to 2 other TFs), indicating that the majority of the TF-TF regulatory networks have simple TF relationships for controlling tissue gene expression.

These results may partially explain the observed statistical redundancy between mammalian TFs; i.e., if one TF is necessary for the recruitment of another, these TFs will provide similar predictive information regarding the regulatory state despite their distinct functional roles.

In addition, we identified protein-DNA interactions between several small cis-regulatory DNA elements and TFs (i.e. to identify consensus TF binding sites).

However, DREM does not infer the logical roles of the TFs (i.e., whether a specific TF is necessary or sufficient for regulating a set of target genes).

An alternative explanation is that there is genetic redundancy among TFs; i.e., deletion of one TF is "buffered" by a second TF that can substitute for the first, e.g., by regulating an overlapping set of genes.