Proteomics in India: A Report on a Brainstorming Meeting at Hyderabad, India
Bibliographic record
Abstract
This meeting was a stimulating gathering of international and Indian experts in the area of proteomics. All attendees mutually benefited from listening to the speaker presentations and the subsequent informal discussions. Topics covered included the development and use of mass spectrometry, sample preparation, chromatography, fragmentation, posttranslational modification identification, and the analysis of the proteomes of organelles, plants, microbes, cancer cells, venoms, and neurons. As more and more proteomic analyses are used in different areas of science, these methods offer the potential of helping in solving problems for all mankind and especially the challenges India faces in agriculture and medicine. With time, genomic-related instruments have become cheaper, so it may be expected that in a similar fashion, proteomic-related instruments will also become cheaper. Thus, in the future, proteomics has the potential to play a greater role in fundamental, translational, and clinical research. It is the young scientists who were in attendance that will carry forth this work, and it was a pleasure to have such a diverse group of speakers at the Hyderabad meeting for them to interact with.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".