ANFIS-based fuzzy systems for searching dna-protein binding sites
Bibliographic record
Abstract
Abstract Transcriptional regulation mainly controls how genes are expressed and how cells behave based on the transcription factor (TF) proteins that bind upstream of the transcription start sites (TSSs) of genes. These TF DNA binding sites (TFBSs) are usually short (5-15 base pairs) and degenerate (some positions can have multiple possible alternatives). Traditionally, computational methods scan DNA sequences using the position weight matrix (PWM) of a given TF, calculate binding scores for each K-mer against the PWM, and finally classify a K-mer as to whether it is a putative TFBS or a background sequence based on a cut-off threshold. The FSCAN system, which is proposed in this paper, employs machine learning techniques to build a learner model that is able to identify TFBSs in a set of bound sequences without the need for a cut-off threshold. Our proposed method utilizes fuzzy inference techniques along with a distribution-based filtering algorithm to predict the binding sites of a TF given its PWM model and phastCons scores for the input DNA sequences. Data imbalance reduction techniques are also used to ease the learning of the adaptive-neuro fuzzy inference system (ANFIS) algorithm. The proposed system is tested on 22 ChIP-chip sequence-sets from the Saccharomyces Cerevisiae genome. Our results show that FSCAN outperforms other approaches like MatInspector and MATCH and is quite robust. As more transcriptional data becomes available, our proposed framework encourages the use of fuzzy logic techniques in the prediction of TFBSs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".