Machine learning in biology - Quiz
A) Microbial communities from environmental DNA samples B) Chemical synthesis of pharmaceutical compounds C) Human genetic disorders and hereditary diseases D) The evolution of complex multicellular organisms
A) Lack of available supercomputers B) High dimensionality of datasets C) Lack of interest from the scientific community D) Inability to collect environmental samples
A) Colorectal cancer B) Diabetes C) Inflammatory bowel disease D) Alzheimer's disease
A) To identify soil bacteria biodiversity B) To sequence the entire human genome C) To predict the climate change impact on ecosystems D) To classify healthy patients and patients with IBD symptoms
A) MegaR B) RF-FVS C) Gradient boosted tree model D) Ph-CNN
A) The speed of the internet connection B) Performance and diversity of decision trees in the ensemble C) The geographic location of the samples D) The number of researchers involved
A) Random forest coupled with forwarding variable selection B) Robust feature with forward validation system C) Recurrent forest with functional variable sequencing D) Random functional variable selection
A) IBD and Diabetes datasets B) Soil and water microbiome data C) CDI and CRC datasets D) Plant-pathogen interaction data
A) A supercomputer specifically for metagenomics B) A neural network architecture C) A new sequencing technology D) An R package for taxonomic profiles and classification models
A) There are no microbial communities in the environment B) Lack of available software C) Data complexity D) Environmental samples are not valuable
A) Hopfield neural networks B) Gaussian neural networks C) Quantum neural networks D) Linear neural networks
A) Genetic mutations in human gut bacteria B) Relationship among soil, microbiome biodiversity, and ecosystem stability C) The speed of DNA sequencing in labs D) Treatment efficacy for colorectal cancer
A) It remains constant B) It decreases linearly C) It disappears completely D) It increases exponentially
A) 75.14% B) 81% C) 99.01% D) 90.17%
A) Nucleotides B) Carbohydrates C) Amino acids D) Lipids
A) Primary chains B) Protein filaments C) Quaternary bonds D) Beta sheets
A) 50-60% B) 95-99% C) 82-84% D) 20-30%
A) Alpha, beta, or gamma B) Helix, sheet, or coil C) Folded, unfolded, or neutral D) Primary, secondary, or tertiary
A) 95–100% B) 88–90% C) 60–65% D) 70–75%
A) Pauling Institute B) Corey Laboratories C) DeepMind D) CASP
A) 2020 B) 1951 C) 2022 D) 2018
A) Lipid membrane synthesis B) Protein loop modeling C) DNA sequencing optimization D) RNA translation errors
A) Metabolic pathway simulation B) Protein contact map prediction C) Cell division modeling D) Genome assembly
A) Hexagonal structure B) Tertiary structure C) Atomic structure D) Pentagonal structure
A) Hierarchical Markov model B) Hidden Markov model C) Heuristic Markov method D) Hidden Machine model
A) External noise B) The underlying state process C) Independent random variables D) The user input
A) Yes B) No C) Only for non-biological data D) Only for discrete states
A) Profiling multiple sequence alignments B) Visual cortex modeling C) Metagenomic filtering D) Image classification
A) Feature map B) Translation window C) Convolution zone D) Receptive field
A) Computational power B) Training data C) Pre-processing D) Validation
A) Pre-determined by the analyst B) Hand-engineered by humans C) Via manual feature extraction D) Through automated learning
A) GC content B) Random selection C) Sequence identity D) Patristic distance
A) Generated by artificial intelligence B) Programmed by hand C) Calculated by physical modeling D) Derived from hardware limitations
A) A numerical value B) An analog signal C) A raw string of data D) A categorical class
A) A numerical valued feature B) A symbolic proof C) A hardware interrupt D) A discrete class label
A) Linear regression B) Principal component analysis C) Data encryption D) Binary serialization
A) 16 thousand B) 4 billion C) 12 thousand D) 16 million
A) Proteomics B) Genomics C) Text mining D) Quantum computing
A) By following pre-defined manual rules B) By ignoring complex dataset patterns C) By combining low-level features into abstract features D) By simulating the organism's physical structure
A) To initialize the weights B) To increase the gradient C) Scaling D) To compress the memory
A) One B) Three C) Two D) Four
A) W1 B) X C) b2 D) Y
A) Principal component analysis B) Neural networking C) Bootstrap aggregating D) Support vector machines
A) Removing outliers from the original dataset B) Calculating gradient descent C) Imputing missing values D) Increasing the number of decision trees
A) Divisive B) Affinity propagation C) Partitional D) Agglomerative
A) They start with one large cluster B) They are always hierarchical C) They cannot be used on large datasets D) They determine all clusters at once
A) K-means B) BIRCH C) Affinity propagation D) K-medoids
A) BIRCH B) MAMMAL C) GeneFormer D) ESMFold2
A) An empty dataset B) The whole set C) Each element as a separate cluster D) A random subset
A) Evo2 B) GeneFormer C) HyenaDNA D) ESMFold2
A) Erroneous interpretation B) Standardized databases C) High-speed processing D) Algorithm efficiency
A) X-ray diffraction B) Paper-based recording C) Optical microscopy D) Manual observation
A) Stock market analysis B) Cancer genomic studies C) Plant hybridization D) Meteorological prediction
A) Soil chemistry B) Acoustic engineering C) Urban planning D) Cellular imaging
A) Transportation logistics B) Energy grid management C) Variant calling D) Hardware manufacturing
A) Natural language processing B) Random guess algorithms C) Physical paper files D) Manual database entry
A) Administrative paperwork B) Geographical location C) Hospital budget limits D) Individual genomic variability
A) Every 18 months B) Every 10 years C) Every 5 years D) Every 6 months
A) Global population trends B) Mechanical vibration C) Atmospheric pressure D) Genome rearrangements
A) Lab-on-a-chip B) Genetic sequencers C) Protein synthesizers D) Cellular microscopes
A) Genetic network induction B) Microchip physical manufacturing C) DNA chemical synthesis D) Biological tissue harvesting
A) Viral replication cycles B) Gene expression C) Cell division frequency D) Protein folding rates
A) Hardware circuit design B) Data storage compression C) Expression pattern identification D) Microchip heat reduction |
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