Bibliographic record
Abstract
The bioinformatics research area is now faced with an obstacle of ever-increasing biological data to verify their biological discovery.As data increases, so does the workload for managing, processing and analysing this data.Combined with the inherent complexity of biological problems, traditional approaches results in long run-time and huge memory requirements.The emergence of accelerator technologies such as multicore architectures provides the opportunity to achieve significant improvements in execution time for many bioinformatics applications, compared to sequential generalpurpose platforms.Using multi-cores to solve large scale bioinformatics applications, such as sequence analysis, is therefore a promising and challenging research field, since large-scale computational bioinformatics problems can benefit much from this kind of processing power.In order to implement efficient and scalable code for this type of architecture, a shift of paradigm in applications development and novel programming techniques are required.In this thesis, we investigate algorithms and techniques on how to efficiently map bioinformatics applications onto a heterogeneous multi-core system, the Cell Broadband Engine (Cell/BE).In particular, we have focused on the following important and widely used applications, i.e. alignment of long DNA sequences, Smith-Waterman algorithm, BLASTP algorithm and pairwise distance matrix computations, which is an integral part of the multiple sequence alignment algorithms such as ClustalW.Aligning long DNA sequences is a common and often repeated task in molecular biology.We have developed a novel, efficient and scalable parallel algorithm for very long DNA sequence alignment on a heterogeneous multi-core system, the Cell Broadband Engine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".