Biomedical subjects

John M Hancock

Publications and source records attributed to John M Hancock.

4 recordsLinked to original sources

PubMed2026

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

Artificial Intelligence↗

PubMed2003

PlantProm: a database of plant promoter sequences.

PlantProm DB, a plant promoter database, is an annotated, non-redundant collection of proximal promoter sequences for RNA polymerase II with experimentally determined transcription start site(s), TSS, from various plant species. The first release (2002.01) of PlantProm DB contains 305 entries including 71, 220 and 14 promoters from monocot, dicot and other plants, respectively. It provides DNA sequence of the promoter regions (-200 : +51) with TSS on the fixed position +201, taxonomic/promoter type classification of promoters and Nucleotide Frequency Matrices (NFM) for promoter elements: TATA-box, CCAAT-box and TSS-motif (Inr). Analysis of TSS-motifs revealed that their composition is different in dicots and monocots, as well as for TATA and TATA-less promoters. The database serves as learning set in developing plant promoter prediction programs. One such program (TSSP) based on discriminant analysis has been created by Softberry Inc. and the application of a support ftp: vector machine approach for promoter identification is under development. PlantProm DB is available at http://mendel.cs.rhul.ac.uk/ and http://www.softberry.com/.

Databases, Nucleic Acid↗

PubMed2002

Genome size and the accumulation of simple sequence repeats: implications of new data from genome sequencing projects.

The relationship between the level of repetitiveness in genomic sequences and genome size has been re-investigated making use of the rapidly growing database of complete eubacterial and archaeal genome sequences combined with the fragmentary but now large amount of data from eukaryotic genomes. Relative simplicity factors (RSFs), which measure the repetitiveness of sequences, were calculated and significantly simple motifs (SSMs), which identify the kinds of sequences that are repeated, were identified. A previously reported correlation between genome size and repetitiveness was confirmed, but it was shown that the higher RSFs seen in eukaryotic genomes also reflect a generally higher level of repetitiveness independent of genome size differences. Differences in genome size are responsible for about 10% of the variance in RSF seen between species. The spectrum of SSMs seen within a genome differed markedly within the eubacteria but less so in eukaryotes and, particularly, in archaea. Species with SSM spectra that differ from the norm tend also to have high RSFs for their genome size and to be pathogens that make use of repetitive sequences to avoid host defence responses. Some of the variance in repetitiveness seen in other species may therefore also reflect the action of selection, although other forces such as variation in the effectiveness of mechanisms for regulating slippage errors of replication, may also be important.

Animals↗

PubMed2002

Detecting cryptically simple protein sequences using the SIMPLE algorithm.

MOTIVATION: Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. RESULTS: We have developed a new tool, based on the SIMPLE algorithm, that facilitates the quantification of the amount of simple sequence in proteins and determines the type of short motifs that show clustering above a certain threshold. By modifying the sensitivity of the program simple sequence content can be studied at various levels, from highly organised tandem structures to complex combinations of repeats. We compare the relative amount of simplicity in different functional groups of yeast proteins and determine the level of clustering of the different amino acids in these proteins. AVAILABILITY: The program is available on request or online at http://www.biochem.ucl.ac.uk/bsm/SIMPLE.

Algorithms↗